Machine Learning in Cyber Security // Monika Venckauskaite // MLOps Meetup #70

MLOps community meetup #70! Last Wednesday, we talked to Monika Venckauskaite, Senior Machine Learning Engineer at Vinted.Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠// AbstractOne of the areas that has the most transformed by ML in these years is cybersecurity. Traditionally, SIEM (Security Intelligence and Event Management) is performed by human analysts. However, as the cyber powers and tools of the world are growing, we need more and more of these specialists. The entire area of cybersecurity is experiencing a shortage of talent. This is where the ML is coming in to help us. Cybersecurity ML systems require a lot of expertise from specialists as well as unique ways of handling user-sensitive data. This imposes various architectural solutions. In this talk, Monika introduces us to the ways of using ML in cybersecurity and the unique challenges we face.// BioMonika is a keen and curious ML engineer, loving to build systems. She started in machine learning as a master's student, looking for the Higgs Boson and Dark matter within the CERN data. Later on, Monika moved to the IT industry and worked on various machine learning projects, including Open Source Intelligence Tools and a distributed system for ML cybersecurity analytics.Currently, Monika works as an MLOps engineer, improving the MLOps platform that is used in production to ship models to a 45 million-user platform. Monika also works in a start-up that is innovating satellite communication. In her free time, she loves books, traveling, and playing music.// TakeawaysCyber threats are all around us. ML as technology is both a savior and a threat.GDPR and sensitive user data bring in extra challenges for cybersecurity intelligence systems, leading to more complex architectural decisions.ML helps to fight the talent shortage.Cybersecurity requires real-time ML systems and reacting ASAP.----------- Connect With Us ✌️-------------   Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerConnect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Monika on LinkedIn: https://www.linkedin.com/in/monika-in-space/Timestamps: [00:00] Introduction to Monika Venčkauskaitė [05:50] Monika's background in tech [08:50] Machine Learning in Cyber Security [09:37] Content [10:19] Our world is run by machines    [11:16] Cybersecurity Threats [12:44] Cybersecurity Incident Response                         Cycle:                         1. Identify           2. Protect           3. Detect           4. Respond           5. Recover[25:05] The Iceberg                          Surface Web - 4% Indexed and easily searchable            Deep Web - 90% Not Indexed, tougher to find            Dark Web - 6% Obscured, difficult to discover[47:45] Recommendation: AI Superpowers: China, Silicon Valley, And The New World Order by Kai-Fu Lee (https://www.amazon.com/AI-Superpowers-China-Silicon-Valley/dp/132854639X)[50:54] "I think we are going in the same direction, but our implementations are different."