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024 7 _a10.1007/978-3-030-30719-6
_2doi
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072 7 _aCOM053000
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245 1 0 _aAdversarial and Uncertain Reasoning for Adaptive Cyber Defense
_h[electronic resource] :
_bControl- and Game-Theoretic Approaches to Cyber Security /
_cedited by Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, Michael Wellman.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aVII, 263 p. 120 illus., 45 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSecurity and Cryptology,
_x2946-1863 ;
_v11830
505 0 _aOverview of Control and Game Theory in Adaptive Cyber-Defenses -- Control Theoretic Approaches to Cyber-Security -- Game-Theoretic Approaches to Cyber-Security: Issues and Challenges and Results -- Reinforcement Learning for Adaptive Cyber Defense against Zero-day Attacks -- Moving Target Defense Quantification -- Empirical Game-Theoretic Methods for Adaptive Cyber-Defense -- MTD Techniques for Memory Protection against Zero-Day Attacks -- Adaptive Cyber Defenses for Botnet Detection and Mitigation -- Optimizing Alert Data Management Processes at a Cyber Security Operations Center -- Online and Scalable Adaptive Cyber Defense.
520 _aToday'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. .
650 0 _aComputer crimes.
_910774
650 0 _aComputer engineering.
_910164
650 0 _aComputer networks .
_931572
650 0 _aComputers.
_98172
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aMathematical statistics.
_99597
650 1 4 _aComputer Crime.
_986411
650 2 4 _aComputer Engineering and Networks.
_991223
650 2 4 _aComputing Milieux.
_955441
650 2 4 _aComputer Communication Networks.
_991224
650 2 4 _aProbability and Statistics in Computer Science.
_931857
700 1 _aJajodia, Sushil.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_991225
700 1 _aCybenko, George.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_991226
700 1 _aLiu, Peng.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_991227
700 1 _aWang, Cliff.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_991228
700 1 _aWellman, Michael.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_991229
710 2 _aSpringerLink (Online service)
_991230
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030307189
776 0 8 _iPrinted edition:
_z9783030307202
830 0 _aSecurity and Cryptology,
_x2946-1863 ;
_v11830
_991231
856 4 0 _uhttps://doi.org/10.1007/978-3-030-30719-6
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