000			01496nam a22001817a 4500
008			250109b |||||||| |||| 00| 0 eng d
020			_a9789811527180
050			_aTK5105.59 _b.S476 2020
100			_aSengupta, Nandita
245			_aIntrusion Detection : _ba data mining approach / _cNandita Sengupta, Jaya Sil
260			_aSingapore: _bSpringer, _c2020.
300			_a136 Pages; _c23 cm.
440			_aCognitive Intelligence and Robotics.
521			_aThis book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.
650			_aIntrusion Detection Systems.
650			_aData Mining.
942			_2lcc _cBK
999			_c6820 _d6820