Adversarial and Uncertain Reasoning for Adaptive Cyber Defense

Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneous functionalities but randomized manifestations.

The 10 papers included in this State-of-the Art Survey present recent advances made by a large team of researchers working on the same US Department of Defense Multidisciplinary University Research Initiative (MURI) project during 2013-2019. This project has developed a new class of technologies called Adaptive Cyber Defense (ACD) by building on two active but heretofore separate research areas: Adaptation Techniques (AT) and Adversarial Reasoning (AR). AT methods introduce diversity and uncertainty into networks, applications, and hosts. AR combines machine learning, behavioral science, operations research, control theory, and gametheory to address the goal of computing effective strategies in dynamic, adversarial environments.

• adaptive cyber defense
• artificial intelligence
• botnets
• clustering
• computer operating systems
• control theory
• cryptography
• cyber security
• data management
• data mining
• data security
• game theory
• information technology
• malware
• moving targets
• nash equilibrium
• security systems
• sensors
• target tracking
• uncertain reasoning

• George Mason University, Fairfax, USA

  Sushil Jajodia

• Dartmouth College, Hanover, USA

  George Cybenko

• Pennsylvania State University, University Park, USA

  Peng Liu

• Army Research Laboratory, Triangle Park, USA

  Cliff Wang

• University of Michigan, Ann Arbor, USA

  Michael Wellman

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