AI in Cybersecurity

Inbunden, Engelska, 2018

Del 151 i serien Intelligent Systems Reference Library

2 121 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

This book presents a collection of state-of-the-art AI approaches to cybersecurity and cyberthreat intelligence, offering strategic defense mechanisms for malware, addressing cybercrime, and assessing vulnerabilities to yield proactive rather than reactive countermeasures.

Produktinformation

Utgivningsdatum:2018-09-27
Mått:155 x 235 x 14 mm
Vikt:488 g
Format:Inbunden
Språk:Engelska
Serie:Intelligent Systems Reference Library
Antal sidor:205
Förlag:Springer International Publishing AG
ISBN:9783319988412

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT
IT-säkerhet inom Data och IT

Mer om författaren

Leslie F. Sikos, Ph.D. is a computer scientist specializing in formal knowledge representation, ontology engineering, and automated reasoning applied to various domains, including cyberthreat intelligence and network applications that require cybersituational awareness. He has worked in both academia and the industry, and acquired hands-on skills with datacenter and cloud infrastructures, cyberthreat management, and firewall configuration. He holds professional certificates and is a member of various industry-leading organizations, such as the ACM, the Association for Automated Reasoning, the IEEE Special Interest Group on Big Data for Cyber Security and Privacy, and the IEEE Computer Society Technical Committee on Security and Privacy.

Innehållsförteckning

OWL Ontologies in Cybersecurity: Conceptual Modeling of Cyber-Knowledge.- Knowledge Representation of Network Semantics for Reasoning-Powered Cyber-Situational Awareness.- The Security of Machine Learning Systems.- Patch Before Exploited: An Approach to Identify Targeted Software Vulnerabilities.- Applying Artificial Intelligence Methods to Network Attack Detection.- Machine Learning Algorithms for Network Intrusion Detection.- Android Application Analysis using Machine Learning Techniques.