Han Yu

Xue Li

Fei Fang

Bohan Li

Lin Yue

Jing Jiang

Weitong Chen

Guodong Long

This book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part II named: Pattern mining; Graph mining; Text mining; Multimedia and time series data mining; and Classification, clustering and recommendation. * The conference was originally planned for December 2021, but was postponed to 2022.

17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II

Advanced Data Mining and Applications

Charles A. Kamhoua

Christopher D. Kiekintveld

Quanyan Zhu

Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against persistent and advanced threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.

Game Theory and Machine Learning for Cyber Security

Bo An

Stefan Rass

Christopher Kiekintveld

Stefan Schauer

This book constitutes the refereed proceedings of the 8th International Conference on Decision and Game Theory for Security, GameSec 2017, held in Vienna, Austria, in October 2017. The 24 revised full papers presented together with 4 short papers were carefully reviewed and selected from 71 submissions.The papers address topics such as Game theory and mechanism design for security and privacy; Pricing and economic incentives for building dependable and secure systems; Dynamic control, learning, and optimization and approximation techniques; Decision making and decision theory for cybersecurity and security requirements engineering; Socio-technological and behavioral approaches to security; Risk assessment and risk management; Security investment and cyber insurance; Security and privacy for the Internet-of-Things (IoT), cyber-physical systems, resilient control systems; New approaches for security and privacy in cloud computing and for critical infrastructure; Security and privacy of wireless and mobile communications, including user location privacy; Game theory for intrusion detection; and Empirical and experimental studies with game-theoretic or optimization analysis for security and privacy.

8th International Conference, GameSec 2017, Vienna, Austria, October 23-25, 2017, Proceedings

Decision and Game Theory for Security

Milind Tambe

Bistra Dilkina

Andrew J. Plumptre

With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization.

Artificial Intelligence and Conservation

Fei Fang

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