000			03900cam a22006498i 4500
001			on1178869780
003			OCoLC
005			20220711203628.0
006			m d u
007			cr |||||||||||
008			200618s2021 nju ob 001 0 eng
010			_a 2020027877
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dYDX _dDG1 _dOCLCO
020			_a9781119606383 _q(electronic bk. : oBook)
020			_a1119606381 _q(electronic bk. : oBook)
020			_a9781119606352 _q(epub)
020			_a1119606357 _q(epub)
020			_a9781119606079 _q(adobe pdf)
020			_a1119606071 _q(adobe pdf)
020			_z9781119606031 _q(cloth)
029	1		_aAU@ _b000067575294
035			_a(OCoLC)1178869780
042			_apcc
050	0	0	_aTJ222
082	0	0	_a629.8/95583 _223
049			_aMAIN
100	1		_aAlmalawi, Abdulmohsen, _eauthor. _99442
245	1	0	_aSCADA security : _bmachine learning concepts for intrusion detection and prevention / _cAbdulmohsen Almalawi, King Abdulaziz University, Zahir Tari, RMIT University, Adil Fahad, Al Baha University, Xun Yi, Royal Melbourne Institute of Technology.
263			_a2108
264		1	_aHoboken, NJ, USA : _bWiley, _c2021.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
490	1		_aWiley series on parallel and distributed computing
505	0		_aIntroduction -- Background -- SCADA-Based Security Testbed -- Efficient k-Nearest Neighbour Approach Based on Various-Widths Clustering -- SCADA Data-Driven Anomaly Detection -- A Global Anomaly Threshold to Unsupervised Detection -- Threshold Password-Authenticated Secret Sharing Protocols -- Conclusion.
504			_aIncludes bibliographical references and index.
520			_a"This book provides insights into issues of SCADA security. Chapter 1 discusses how potential attacks against traditional IT can also be possible against SCADA systems. Chapter 2 gives background information on SCADA systems, their architectures, and main components. In Chapter 3, the authors describe SCADAVT, a framework for a SCADA security testbed based on virtualization technology. Chapter 4 introduces an approach called kNNVWC to find the k-nearest neighbours in large and high dimensional data. Chapter 5 describes an approach called SDAD to extract proximity-based detection rules, from unlabelled SCADA data, based on a clustering-based technique. In Chapter 6, the authors explore an approach called GATUD which finds a global and efficient anomaly threshold. The book concludes with a summary of the contributions made by this book to the extant body of research, and suggests possible directions for future research"-- _cProvided by publisher.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
590			_bWiley Frontlist Obook All English 2020
650		0	_aSupervisory control systems. _99443
650		0	_aAutomatic control _xSecurity measures. _99444
650		0	_aIntrusion detection systems (Computer security) _99445
650		0	_aMachine learning. _91831
650		7	_aIntrusion detection systems (Computer security) _2fast _0(OCoLC)fst01762593 _99445
650		7	_aMachine learning _2fast _0(OCoLC)fst01004795 _91831
650		7	_aSupervisory control systems _2fast _0(OCoLC)fst01139089 _99443
655		4	_aElectronic books. _93294
700	1		_aTari, Zahir, _eauthor. _99446
700	1		_aFahad, Adil, _eauthor. _99447
700	1		_aYi, Xun, _eauthor. _99448
776	0	8	_iPrint version: _aAlmalawi, Abdulmohsen. _tSCADA security _dHoboken, NJ, USA : Wiley, 2021. _z9781119606031 _w(DLC) 2020027876
830		0	_aWiley series on parallel and distributed computing. _94669
856	4	0	_uhttps://doi.org/10.1002/9781119606383 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c69399 _d69399