TY - BOOK ID - 8581684 TI - Machine Learning in Cyber Trust : Security, Privacy, and Reliability AU - Tsai, Jeffrey J. P. AU - Yu, Philip S. PY - 2009 SN - 1441946985 0387887342 9786612126956 1282126954 0387887350 PB - New York, NY : Springer US : Imprint: Springer, DB - UniCat KW - Algorithms. KW - Computer security. KW - Cyberterrorism -- Prevention. KW - Machine learning. KW - Cyberterrorism KW - Prevention. KW - Learning, Machine KW - Attacks on computers KW - Computer attacks KW - Cyber attacks KW - Cyber terrorism KW - Cyber war KW - Cyberwarfare KW - Computer privacy KW - Computer system security KW - Computer systems KW - Computers KW - Cyber security KW - Cybersecurity KW - Electronic digital computers KW - Security of computer systems KW - Security measures KW - Protection of computer systems KW - Protection KW - Computer science. KW - Data mining. KW - Artificial intelligence. KW - Computer Science. KW - Systems and Data Security. KW - Data Mining and Knowledge Discovery. KW - Artificial Intelligence (incl. Robotics). KW - Data protection KW - Security systems KW - Hacking KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Informatics KW - Science KW - Artificial intelligence KW - Computer crimes KW - Terrorism KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:8581684 AB - Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems turns out to be a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the security, privacy, and reliability issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state of the practice in this important area, and giving a classification of existing work. Specific features include the following: A survey of various approaches using machine learning/data mining techniques to enhance the traditional security mechanisms of databases A discussion of detection of SQL Injection attacks and anomaly detection for defending against insider threats An approach to detecting anomalies in a graph-based representation of the data collected during the monitoring of cyber and other infrastructures An empirical study of seven online-learning methods on the task of detecting malicious executables A novel network intrusion detection framework for mining and detecting sequential intrusion patterns A solution for extending the capabilities of existing systems while simultaneously maintaining the stability of the current systems An image encryption algorithm based on a chaotic cellular neural network to deal with information security and assurance An overview of data privacy research, examining the achievements, challenges and opportunities while pinpointing individual research efforts on the grand map of data privacy protection An algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data An approach for assessing the reliability of SOA-based systems using AI reasoning techniques The models, properties, and applications of context-aware Web services, including an ontology-based context model to enable formal description and acquisition of contextual information pertaining to service requestors and services Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks. ER -