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- Learning to Win: Making the Case for Autonomous Cyber Security Solutions

| Presenter: Dr. Robert Wright | 5 Comments

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Machine learning is revolutionizing many industries and forever changing how we interact with computer systems and each other. The revolution is particularly relevant to cyber security, where machine learning is used to help human analysts make sense of increasingly large amounts of data to identify threats and mitigate vulnerabilities. While this symbiotic relationship is improving our security posture, it is fundamentally limited by humans making decisions at human speed. Significant gains in security can be realized if autonomy is embraced and “trusted” machine learning is given license to make actionable decisions at machine speed. Despite the potential benefits, there have been relatively few research efforts and applications of autonomous machine learning towards cyber security. This webinar describes the benefits of machine learning based approaches for autonomous control in the cyber domain. We discuss emerging autonomous machine learning technologies and their recent successes, technical and non-technical challenges to overcome, and potential near-term applications to cyber security.

Read the companion CSIAC Report: https://www.csiac.org/csiac-report/learning-to-win-making-the-case-for-autonomous-cyber-security-solutions/

 

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Presenter

Dr. Robert Wright
Dr. Robert Wright
Dr. Robert Wright, AIS, Senior Advising Engineer Binghamton University, Ph.D., Computer Science, 2014 Dr. Robert Wright brings 15 years of proven experience developing advanced machine learning technologies that deliver revolutionary capabilities for the DoD. Dr. Wright is a senior advising research scientist with AIS and a Principal Investigator for the VIC3TORS effort, applying his machine learning expertise to advance biometric verification and attribution. He received his Ph.D. in computer science from Binghamton University in 2014 for his work in developing reinforcement algorithms that learn efficiently from experience. He has published 10+ papers and journal articles on machine learning in top tier scientific venues and was awarded "Best Paper" at the 2013 European Conference on Machine Learning. Prior to AIS, Dr. Wright was a research scientist with the AFRL and co-lead for the Autonomy Community of Interest: Machine Perception Reasoning and Intelligence technical challenge area for the Office of the Secretary of Defense. In his 13 years with AFRL, he led several research efforts in machine learning and autonomous systems. His research interests and areas of expertise include reinforcement learning, deep learning, data mining, biometrics, multi-agent systems, and genetic algorithms.

Reader Interactions

Ask a Question or Comment:

  2. This question was asked during the webinar:
    Why is it that reinforcement learning can greatly improve cyber security? Can you comment more on the correlation between these two aspects?

  3. This question was asked during the webinar:
    Many Cyber Security concerns are not well defined in practice, take for example monitoring an organization ecosystem, how do you think the Autonomous Cyber Security problem can be framed?

  4. This question was asked during the webinar:
    Is there a limit to the reinforcement capacity of algorithms, i.e. if you pit a “Blue Team” algorithm against a “Red Team” algorithm, it would sound as if they would eventually exhaust all actions until a clear winner emerged, at which point learning would end. Can those algorithms then be transported to a different “scenario” and further progressed in their learning?

  5. From Presenter:
    We tried to keep the slides fairly high-level to provide an introduction to these topics, so if you would like to discuss anything in more depth, want some resources to learn more, or have any further questions please reach out by submitting a comment here!

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