Titlebook: Autonomous Cyber Deception; Reasoning, Adaptive Ehab Al-Shaer,Jinpeng Wei,Cliff Wang Textbook 2019 Springer Nature Switzerland AG 2019 cyb

microscopic

Rectilinear Planarity of?Partial 2-Trees game theories have considered incomplete information to consider uncertainty, how players’ different perceptions or misperceptions can affect their decision-making has not been fully addressed. In particular, we discuss . which has been used to resolve conflicts under uncertainty. In this chapter,

critique

Jacob Miller,Vahan Huroyan,Stephen Kobourovtems vulnerable to targeted attacks that are deceptive, persistent, adaptive, and strategic. Attack instances such as Stuxnet, Dyn, and WannaCry ransomware have shown the insufficiency of off-the-shelf defensive methods including the firewall and intrusion detection systems. Hence, it is essential t

FEIGN

Tarik Crnovrsanin,Jacqueline Chu,Kwan-Liu Masignificant confusion in discovering and targeting cyber assets. One of the key objectives for cyber deception is to hide the true identity of the cyber assets in order to effectively deflect adversaries away from critical targets, and detect their activities early in the kill chain..Although many c

Preamble

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Fabian Lipp,Alexander Wolff,Johannes Zinkf these new entrants to the market lack security engineering experience and focus heavily on time-to-market. As a result, many home and office networks contain IoT devices with security flaws and no clear path for security updates, making them attractive targets for attacks, e.g., recent IoT-centric

lethal

Finasteride

Lecture Notes in Computer Sciencey to infect only targeted computers, etc. If we are able to extract the system resource constraints from malware binary code, and manipulate the environment state as ., we would then be able to deceive malware for defense purpose, e.g., immunize a computer from infections, or trick malware into beli

形容詞

Using Deep Learning to Generate Relational HoneyDatay little attention. In this book chapter, we discuss our secure deceptive data generation framework that makes it hard for an attacker to distinguish between the real versus deceptive data. Especially, we discuss how to generate such deceptive data using deep learning and differential privacy techni

PALMY