Cybersecurity

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• Case Studies
  • Risk, Compliance and Regulation
    • Screening; Passenger, Luggage, & Cargo
  • Government Services
  • Defense
• Capabilities
• Cybersecurity References
• Offense - Adversarial Threats/Attacks
• Cybersecurity Frameworks, Architectures & Roadmaps
  • Cybersecurity Reference Architecture | Microsoft
• Cybersecurity Companies/Products
• Cybersecurity: National Institute of Standards and Technology (NIST) & U.S. Department of Homeland Security (DHS)
• Defense: Cybersecurity and Acquisition Lifecycle Integration Tool (CALIT)
• Cybersecurity: Evaluating & Selling
• (Artificial) Immune System
• 5G Security
• Useful Models ...find outliers:
  • One-class Support Vector Machine (SVM)
  • Variational Autoencoder (VAE)
  • Autoencoder (AE) / Encoder-Decoder
  • Radial Basis Function Network (RBFN)
  • Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
• Detecting Malicious Requests with Keras & TensorFlow | Adam Kusey - Medium
• Best security software: How 12 cutting-edge tools tackle today's threats | CSO
• graphistry.com
• Intelligence Advanced Research Projects Activity (IARPA)Is Trying Keep Adversaries From Corrupting AI Tools ... Could cyber adversaries be training the government’s artificial intelligence tools to fail? | Jack Corrigan - Nextgov
  • TrojAI - Office of the Director of National Intelligence Office: Intelligence Advanced Research Projects Activity FedBizOpps.gov predict whether AI systems have been corrupted through so-called “Trojan attacks.”
• Adversarial Attacks on Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
• Breaking Down the Tencent 2018 Cybersecurity Report
• Chronicle combines all the best parts of Google and X culture
• Fraud and Anomaly Detection | Chris Nicholson - A.I. Wiki pathmind
• The Cyber Security Evaluation Tool (CSET®) | National Cybersecurity and Communications Integration Center ...provides a systematic, disciplined, and repeatable approach for evaluating an organization’s security posture
• Watch me Build a Cybersecurity Startup | Siraj Raval

How to Plan for and Implement a Cybersecurity Strategy Planning and implementing a security strategy to protect a hybrid of on-premises and cloud assets against advanced cybersecurity threats is one of the greatest challenges facing information security organizations today. Join Lex Thomas as he welcomes back Mark Simos to the show as they discuss how Microsoft has built a robust set of strategies and integrated capabilities to help you solve these challenges so that you can build a better understanding how to build an identity security perimeter around your assets. What does a Cybersecurity architecture guide look like? What does the Cybersecurity landscape look like? What does the evolution of IT and Microsoft Security look like? What does a platform security approach look like? Can you describe the benefits of an Integrated Security Experience? How do you measure security success?

Nicolas Papernot - Private Machine Learning with PATE - Cybersecurity With The Best 2017 Cybersecurity With The Best hosted over 40 speakers and hundreds of attendees from all over the world on a single platform on October 14-15, 2017. Nicolas Papernot is a PhD student in Computer Science and Engineering advised by Dr. Patrick McDaniel at the Pennsylvania State University. His research interests lie at the intersection of computer security and deep learning. He is supported by a Google PhD Fellowship in Security.

Confessions of a cyber spy hunter | Eric Winsborrow | TEDxVancouver With over 20 years of experience leading high technology companies out of Silicon Valley, Eric has played a part in shaping the industry as an executive at heavyweights like McAfee, Symantec, and Cisco. Today, he is the CEO of ZanttZ, a company that is developing stealth cyber security technology solutions. Eric and his company are at the forefront of the latest developments in the world of global espionage, and the merging of man and machine. He attests that the Hollywood-created image of the daring secret agent sneaking into a foreign government's laser protected server room to steal top secret information couldn't be further from modern reality. In fact, the "James Bond" of the 21st century doesn't just use a computer, he is the computer. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Wiretapping the Secret Service can be easy and fun | Bryan Seely | TEDxKirkland In 2014, Bryan Seely hacked the Secret Service and the FBI, and then turned himself in to alert authorities to the problem. He'll tell you what you can do about it in his very entertaining talk. Bryan Seely has garnered a great deal of national attention in the last year. He recognized there were some critical problems with the websites we use most often. He learned that not only is our personal security threatened, but so is our national security. To prove his point, Seely hacked the Secret Service and the FBI last year, and then turned himself in to alert authorities to the problem. He'll tell you what you can do about it in his very entertaining talk. 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

Evolution of AI Bots for Real-Time Adaptive Security Thomas Caldwell, Sr. Director, WEBROOT AI bots historically emerged as chatbots. Early versions were rule-based, adding NLP and machine learning turned them into AI-based bots to be used in sales and support. Thousands of times faster than humans without fatigue, these AI bots have arrived. They are now being applied to Real-Time Adaptive Security. Analyzing cross domain context, events and weeding out false positives in milliseconds.

AI gets smart about cybersecurity Read the CNET cybersecurity article here - http://cnet.co/2gxtOGn Cyberattacks have become more sophisticated and more dangerous. Here's how Las Vegas stays safe. Subscribe to CNET: http://cnet.co/2heRhep Check out our playlists: http://cnet.co/2g8kcf4 Like us on Facebook: http://www.facebook.com/cnet

How Israel Rules The World Of Cyber Security | VICE on HBO U.S. intelligence agencies accuse Russia of hacking the 2016 presidential election, a Ben Ferguson travels to Tel Aviv to find out how Israel is on its way to becoming the world's top cyber superpower. Subscribe to VICE News here: http://bit.ly/Subscribe-to-VICE-News Check out VICE News for more: http://vicenews.com Follow VICE News here: Facebook: https://www.facebook.com/vicenews

Machine Learning Techniques for Cyber Security An introduction to machine learning and its applications in cyber-security. Presented by Vahid Behzadan for the OWASP Nettacker team.

Semi Supervised Learning In An Adversarial Environment DataWorks Summit

How the Future of Cybersecurity Depends on AI/ML SparkCognition's Director of Cybersecurity, Rick Pither, discusses the role of artificial intelligence and machine learning in the cyber security landscape. For more information on AI in cybersecurity visit: http://bit.ly/2Vdzj0j

How big data and AI saved the day: critical IP almost walked out the door Cybersecurity threats have evolved beyond what traditional SIEMs and firewalls can detect. We present case studies highlighting how: An advanced manufacturer was able to identify new insider threats, enabling them to protect their IP A media company’s security operations center was able to verify they weren’t the source of a high-profile media leak. The common thread across these real-world case studies is how businesses can expand their threat analysis using security analytics powered by artificial intelligence in a big data environment. Cybersecurity threats increasingly require the aggregation and analysis of multiple data sources. Siloed tools and technologies serve their purpose, but can’t be applied to look across the ever-growing variety and volume of traffic. Big data technologies are a proven solution to aggregating and analyzing data across enormous volumes and varieties of data in a scalable way. However, as security professionals well know, more data doesn’t mean more leads or detection. In fact, all too often more data means slower threat hunting and more missed incidents. The solution is to leverage advanced analytical methods like machine learning. Machine learning is a powerful mathematical approach that can learn patterns in data to identify relevant areas to focus. By applying these methods, we can automatically learn baseline activity and detect deviations across all data sources to flag high-risk entities that behave differently from their peers or past activity. Speaker Roy Wilds Principal Data Scientist Interset

Introduction to Graphistry Graphistry transforms the speed and depth of modern investigations. This unique investigation platform allows analysts to bring together all of their tools and data into a single environment where they can see connections, outliers, progression, and scope of security events. Key capabilities include: - Automatically connects and queries across any and all data sources including SIEMs, Spark, Hadoop, threat feeds, or any source with an API. - Displays data as interactive and intuitive graphs that allow analysts to quickly see important connections, follow leads, and pivot to new data sources on the fly. - Allows analysts to save and share complete investigation workflows as Visual Playbooks that can be reused and embedded wherever they are needed. Learn more at http://www.graphistry.com

"Security: Computing in an Adversarial Environment," Carrie Gates Apr. 12, 2012: "Security: Computing in an Adversarial Environment," presented by Carrie Gates, CA Labs; moderated by Christopher W. Clifton, Purdue University. Security is inherently different from other aspects of computing due to the presence of an adversary. As a result, identifying and addressing security vulnerabilities requires a different mindset from traditional engineering. Proper security engineering—or the lack of it!—affects everything from website scripts to supply chain management to electronic health records to social networks to mobile phones...and the list goes on. Security is further complicated by the translation of social notions—such as identity and trust— into an online world. Worse, security itself is often viewed by both developers and users as the adversary! This learning webinar will introduce the fundamentals of security, describe the security mindset, and highlight why achieving security is difficult. What you'll learn: The security mindset what it is, why it's needed The social side of security usability, adoption, identity, trust A deeper dive on insider threat as a case study what it is, how to detect it, how to prevent it Presenter: Carrie Gates, Senior Vice President and Director of Research, CA Labs Dr. Gates has opened new avenues for collaboration in the field of cyber security for CA Technologies by leveraging government programs that further research between CA Labs and academia. She has given over 20 invited talks internationally, authored more than 40 peer-reviewed publications related to information security, and co-authored an amendment on cloud security research for the America Competes Act that was signed into law in December 2010. In October 2010, Dr. Gates was recognized for her work with a Women of Influence award from CSO magazine. Moderator: Christopher W. Clifton, Associate Professor of Computer Science, Purdue University Dr. Clifton works on data privacy, particularly with respect to analysis of private data.

Deep Learning For Realtime Malware Detection - Domenic Puzio and Kate Highnam Domain generation algorithm (DGA) malware makes callouts to unique web addresses to avoid detection by static rules engines. To counter this type of malware, we created an ensemble model that analyzes domains and evaluates if they were generated by a machine and thus potentially malicious. The ensemble consists of two deep learning models – a convolutional neural network and a long short-term memory network, both which were built using Keras and Tensorflow. These deep networks are flexible enough to learn complex patterns and do not require manual feature engineering. Deep learning models are also very difficult for malicious actors to reverse engineer, which makes them an ideal fit for cyber security use cases. The last piece of the ensemble is a natural-language processing model to assess whether the words in the domain make sense together. These three models are able to capture the structure and content of a domain, determining whether or not it comes from DGA malware with very high accuracy. These models have already been used to catch malware that vendor tools did not detect. Our system analyzes enterprise-scale network traffic in real time, renders predictions, and raises alerts for cyber security analysts to evaluate. Domenic Puzio is a Data Engineer with Capital One. He graduated from the University of Virginia with degrees in Mathematics and Computer Science. On his current project he is a core developer of a custom platform for ingesting, processing, and analyzing Capital One’s cyber-security data sources. Built entirely from opensource tools (NiFi, Kafka, Storm, Elasticsearch, Kibana), this framework processes hundreds of millions of events per hour. Currently, his focus is on the creation and productionization of machine learning models that provide enrichment to the data being streamed through the system. He is a contributor to two Apache projects. Kate Highnam has a background in Computer Science and Business, focusing on security, embedded devices, and accounting. At the University of Virginia, her thesis was a published industrial research paper containing an attack scenario and repair algorithm for drones deployed on missions with limited ground control contact. After joining Capital One as a Data Engineer, Kate has developed features within an internal DevOps Pipeline and Data Lake governance system. Currently, she builds machine learning models to assist cybersecurity experts and enhance defenses.

Fraud Detection

• Introduction to Fraud Detection Systems | Miguel Gonzalez-Fierro, Microsoft
• AI for Health Insurance Fraud Detection – Current Applications | Niccolo Mejia

Thwart Fraud Using Graph Enhanced Artificial Intelligence Amy Hodler, Analytics Program Manager at Neo4j and Scott Heath, Graph Practice Lead at Expero: This webinar will help you understand how successful financial services, banks and retailers are using graph technology and embedding intelligence to quickly identify risk and fraud patterns as they evolve. Fraudsters are now using more sophisticated and dynamic methods for credit card, money laundering and other types of fraud. Leveraging graph technology will allow you to see beyond individual data points and uncover difficult-to-detect patterns. Hear how to maximize time and resources with graph technology vs. traditional approaches.

AI has helped us prevent billions in fraud: Mastercard’s Ed McLaughlin Mastercard President of Operations and Technology Ed McLaughlin discusses the upcoming artificial intelligence conference at the White House and how his company is utilizing AI.

Build A Complete Project In Machine Learning | Credit Card Fraud Detection 2019 | Eduonix Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course For this project, we will be using the several methods of Anomaly detection with Probability Densities. Artificial Intelligence and Machine Learning E-degree - http://bit.ly/34tCH6S We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! Get access similar 5 more projects here in this with certification- http://bit.ly/2Q2dX3Q You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML You can even check FREE course on Predict Board Game Reviews with Machine Learning on http://bit.ly/2Wm2uKW Learn Machine Learning By Building Projects -http://bit.ly/2ZNkK5T Machine Learning For Absolute Beginners -http://bit.ly/2Q2pNe7

Leveraging Machine Learning for Fraud Analytics (Cloud Next '18) We will showcase how we can build advance accelerators for Fraud Analytics solutions leveraging Google Stack. We will demonstrate how these accelerators fill the gaps that exists within other Fraud Analytics solutions currently available in the market today and how it can offer several benefits including real-time processing, increased accuracy, scalable database and high performance. MLAI102 Event schedule http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub

Data Center Security

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