From physical security to cybersecurity
By Arunesh Sinha, T. Nguyen, Debarun Kar, Matthew Brown, Milind Tambe, Albert Xin Jiang. 2015
57 citations recorded by the source corpus.
Security is a critical concern around the world. In many domains from cybersecurity to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the importance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Computational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of security resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games.” The research challenges posed by these applications include scaling up security games to real-world-sized problems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries. In cybersecurity domain, the interaction between the defender and adversary is quite complicated with high degree of incomplete information and uncertainty. While solutions have been proposed for parts of the problem space in cybersecurity, the need of the hour is a comprehensive understanding of the whole space including the interaction with the adversary. We highlight the innovations in security games that could be used to tackle the game problem in cybersecurity.
Subfields: Civil and Structural Engineering, Control and Systems Engineering, Information Systems, Privacy & Security
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Citations in the corpus
- Towards the Science of Security and Privacy in Machine Learning (2016, background): “Stackelberg games also allow for scalability compared to the corresponding simultaneous move game [90], [91].”
- SoK: Security and Privacy in Machine Learning (2018, background): “egy (in this paper the classifier) and the adversary responds optimally (choose a least cost evasion). Stackelberg games also allow for scalability compared to the corresponding simultaneous move game [90], [91]. Bruckner et al. [92] first recognized that test data manipulation can be seen in the PAC ”