Andere Kunden interessierten sich auch für
![Detection of Intrusions and Malware, and Vulnerability Assessment]( "Detection of Intrusions and Malware, and Vulnerability Assessment")
Detection of Intrusions and Malware, and Vulnerability Assessment55,99 €![Security and Privacy in Communication Networks]( "Security and Privacy in Communication Networks")
Security and Privacy in Communication Networks37,99 €![Security and Privacy in Communication Networks]( "Security and Privacy in Communication Networks")
Security and Privacy in Communication Networks37,99 €![Security and Privacy]( "Security and Privacy")
Security and Privacy37,99 €![ICT Systems Security and Privacy Protection]( "ICT Systems Security and Privacy Protection")
ICT Systems Security and Privacy Protection74,99 €![Cyber Security Cryptography and Machine Learning]( "Cyber Security Cryptography and Machine Learning")
Cyber Security Cryptography and Machine Learning37,99 €![Cyber Security Cryptography and Machine Learning]( "Cyber Security Cryptography and Machine Learning")
Cyber Security Cryptography and Machine Learning49,99 €
Today'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, andgametheory to address the goal of computing effective strategies in dynamic, adversarial environments.
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, andgametheory to address the goal of computing effective strategies in dynamic, adversarial environments.