Tyler Cody – författare

A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization’s cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You’ll also find: A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scalingComprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph constructPractical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectivelyComplete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenariosPerfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.

A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity

In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization’s cyber posture with RL and illuminate the most probable adversarial attack paths in your networks.

Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You’ll also find:

A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scaling Comprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph construct Practical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectively Complete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios

Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.

A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity

In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization’s cyber posture with RL and illuminate the most probable adversarial attack paths in your networks.

Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You’ll also find:

A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scaling Comprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph construct Practical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectively Complete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenarios

Perfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.

This unique monograph explores the science and technology of touch, bridging human tactile perception and advanced haptic interfaces. Aimed at researchers and designers in haptics, it shows how understanding skin biomechanics leads to more intuitive haptic systems. Starting with how mechanical signals processed by the skin produce tactile sensations, it emphasizes the importance of skin deformation patterns correlated with psychophysical responses, highlighting individual differences in tactile sensitivity. It presents practical design principles for sensors and actuators that replicate natural touch and discusses system integration using AI models to predict user experiences and adapt haptic feedback to individual needs. Additionally, it explores designing artificial skin that emulates human skin behavior and natural sensory processing. Concluding with future directions, it advocates for human-centered systems offering consistent tactile experiences across user groups to enhance accessibility and usability.Dr. Bingxu Li is Chief Technology Officer at SmartHap, a haptic technology company based in Virginia, USA.  Dr. Tyler Cody is Chief Executive Officer at SmartHap, as well as Associate Professor of Data Science at the University of Virginia in Charlottesville, Virginia, USA, where he serves as Director of the Artificial General Intelligence Lab.