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Titlebook: Cyber Deception; Techniques, Strategi Tiffany Bao,Milind Tambe,Cliff Wang Book 2023 This is a U.S. government work and not under copyright

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發(fā)表于 2025-3-21 18:39:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Cyber Deception
副標(biāo)題Techniques, Strategi
編輯Tiffany Bao,Milind Tambe,Cliff Wang
視頻videohttp://file.papertrans.cn/242/241707/241707.mp4
概述Presents a comprehensive perspective in cyber deception and state of art in cyber deception research.Provides necessary elements related to cyber deception from cybersecurity techniques.Offers multi-d
叢書名稱Advances in Information Security
圖書封面Titlebook: Cyber Deception; Techniques, Strategi Tiffany Bao,Milind Tambe,Cliff Wang Book 2023 This is a U.S. government work and not under copyright
描述This book introduces recent research results for cyber deception, a promising field for proactive cyber defense. The beauty and challenge of cyber deception is that it is an interdisciplinary research field requiring study from techniques and strategies to human aspects.?.This book covers a wide variety of cyber deception research, including game theory, artificial intelligence, cognitive science, and deception-related technology. Specifically, this book addresses three core elements regarding cyber deception:?.?.Understanding human’s cognitive behaviors in decoyed network scenarios?.Developing effective deceptive strategies based on human’s behaviors.Designing deceptive techniques that supports the enforcement of deceptive strategies.The research introduced in this book identifies the scientific challenges, highlights the complexity and inspires the future research of cyber deception..Researchers working in cybersecurity and advanced-level computer science students focused on cybersecurity will find this book useful as a reference. This book also targets professionals working in cybersecurity..Chapter ‘Using Amnesia to Detect Credential Database Breaches‘ and Chapter ‘Deceiving ML
出版日期Book 2023
關(guān)鍵詞Cybersecurity; Cyber deception; Honeypots; Human/Computer Interactions; Cognitive model; Game theory; Secu
版次1
doihttps://doi.org/10.1007/978-3-031-16613-6
isbn_softcover978-3-031-16615-0
isbn_ebook978-3-031-16613-6Series ISSN 1568-2633 Series E-ISSN 2512-2193
issn_series 1568-2633
copyrightThis is a U.S. government work and not under copyright protection in the U.S.; foreign copyright pro
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Suparna Kailash,Manju Mehta,Rajesh Sagar optimize a deceptive defense based on camouflaging network and system attributes have shown effective numerical results on simulated data. However, these models possess a fundamental drawback due to the assumption that an attempted attack is always successful—as a direct consequence of the deceptiv
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Michelle Sunico-Segarra,Armin Segarrauction to cognitive modeling techniques and concepts, including general goals, capabilities and limitations, cognitive architectures, and instance-based learning. It establishes that cognitive models’ reliance on generative mechanisms has predictive capabilities beyond those of purely data-driven te
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發(fā)表于 2025-3-22 09:49:20 | 只看該作者
Michelle Sunico-Segarra,Armin Segarrase resources in security situations is modeled through Stackelberg Security Games, where defenders allocate the security resources using the schedule from a Strong Stackelberg Equilibrium (SSE) before attackers exploit the network. To further strengthen the security allocation and reduce the costs o
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Colin J. Theaker,Graham R. Brookes step in this kill chain is network reconnaissance, which has historically been active (e.g., network scans) and therefore detectable. However, new networking technology increases the possibility of . network reconnaissance, which is largely undetectable by defenders. In this chapter, we begin by in
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https://doi.org/10.1007/978-3-030-43473-1ch that it deceives the attacker while still permitting the system to perform its intended function. We develop techniques to achieve such deception by studying a proxy problem: malware detection..Researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not
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