Leveraging AI to Address Global Security Challenges

Date/Time: February 26 10:30 AM – 11:30 AM AEDT (February 25 6:30 PM – 7:30 PM ET)

Recording: https://webcast.csiro.au/#/videos/35761881-a504-448c-af9f-4f5345c9c81e

Slides:VS-CSIRO-Feb2021

Guest Speaker:

Professor V.S. Subrahmanian, Dartmouth College, USA.

V.S. Subrahmanian is the Dartmouth College Distinguished Professor in Cybersecurity, Technology, and Society and Director of the Institute for Security, Technology, and Society at Dartmouth. He previously served as a Professor of Computer Science at the University of Maryland from 1989-2017 where he also served for 6+ years as Director of the University of Maryland’s Institute for Advanced Computer Studies. Prof. Subrahmanian is an expert on big data analytics including methods to analyze text/geospatial/relational/social network data, learn behavioral models from the data, forecast actions, and influence behaviors with applications to cybersecurity and counter-terrorism.  He has written five books, edited ten, and published over 300 refereed articles. He is a Fellow of the American Association for the Advancement of Science and the Association for the Advancement of Artificial Intelligence and has received numerous other honors and awards. His work has been featured in numerous outlets such as the Baltimore Sun, the Economist, Science, Nature, the Washington Post, American Public Media and more. He serves on the editorial boards of numerous journals including Science, and currently serves on the Board of Directors of SentiMetrix, Inc. and on the Research Advisory Board of Tata Consultancy Services. He previously served on the Board of Directors of the Development Gateway Foundation (set up by the World Bank),  DARPA’s Executive Advisory Council on Adva

Abstract:

‘I will start this talk with a brief description of 3 applications in which we developed AI-based approaches to some global challenges. First, I briefly describe how we can use AI techniques to destabilize terrorist networks such as Hamas, Hezbollah, and Al-Qaeda.  I then briefly discuss how AI techniques can be used to help reduce incidents of poaching. The third vignette will discuss how AI can be leveraged to deter theft of intellectual property. The main part of the talk will go in-depth into a problem that faces leading governments worldwide.  When a government cyber-warfare unit discovers a new vulnerability, should they disclose it to the vendor who produced the vulnerable product? Or should they “stockpile” the vulnerability, holding it for developing exploits (i.e. cyber weapons) that can be targeted at an adversary? Choosing the first option may be important when the affected company is a corporation in the nation state that discovers the vulnerability and/or if that nation state would have a big exposure to that vulnerability. Choosing the second option has obvious advantages to the discovering nation’s defense. We formulate the cyber competition between countries as a repeated cyber warfare game (RCWG), where two countries (players) compete over a series of vulnerabilities by deciding, at the time of vulnerability discovery, (i) whether to exploit or disclose it and (ii) how long to exploit it if they decide to exploit. We define the equilibrium state of the RCWG as a pure strategy Nash equilibrium, and propose a learning-while-competing framework to compute the pure strategy Nash equilibrium of the formulated RCWG. Though testing our results with real data in the murky world of cyber warfare is challenging, we were able to obtain real statistics from other sources and demonstrate the effectiveness of our proposed algorithm through a set of simulation results under different scenarios using these third party statistics. We conclude with a discussion of how these results may affect the growing cyber-competition between the US and China.‘ Professor V.S. Subrahmanian