Adversarial and Uncertain Reasoning for Adaptive Cyber Defense 1st edition by Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, Michael Wellman ISBN 3030307182 9783030307189

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Authors:Sushil Jajodia; George Cybenko; Peng Liu; Cliff Wang; Michael Wellman , Series:Cyber Security [147] , Tags:Computers; Security; General; Networking; Human-Computer Interaction (HCI); Online Services; Mathematical & Statistical Software; Information Technology; Mathematics; Discrete Mathematics , Author sort:Jajodia, Sushil & Cybenko, George & Liu, Peng & Wang, Cliff & Wellman, Michael , Ids:Google; 9783030307196; DOI , Languages:Languages:eng , Published:Published:Aug 2019 , Publisher:Springer Nature , Comments:Comments: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 game theory to address the goal of computing effective strategies in dynamic, adversarial environments.