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008			220422s2022 sz | s |||| 0|eng d
020			_a9783030941789
024	7		_a10.1007/978-3-030-94178-9 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aTK7895.E42
072		7	_aUKM _2bicssc
072		7	_aTEC008010 _2bisacsh
072		7	_aUKM _2thema
090			_aTK7895.E42EBK
245	1	0	_aMachine Learning for Embedded System Security _h[electronic resource] / _cedited by Basel Halak.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Machine Learning for Tamper Detection -- Machine Learning for IC Counterfeit Detection and Prevention -- Machine Learning for Secure PUF Design -- Machine Learning for Malware Analysis -- Machine Learning for Detection of Software Attacks -- Conclusions and Future Opportunities. .
520			_aThis book comprehensively covers the state-of-the-art security applications of machine learning techniques. The first part explains the emerging solutions for anti-tamper design, IC Counterfeits detection and hardware Trojan identification. It also explains the latest development of deep-learning-based modeling attacks on physically unclonable functions and outlines the design principles of more resilient PUF architectures. The second discusses the use of machine learning to mitigate the risks of security attacks on cyber-physical systems, with a particular focus on power plants. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities. Discusses emerging technologies used to develop intelligent tamper detection techniques, using machine learning; Includes a comprehensive summary of how machine learning is used to combat IC counterfeit and to detect Trojans; Describes how machine learning algorithms are used to enhance the security of physically unclonable functions (PUFs); It describes, in detail, the principles of the state-of-the-art countermeasures for hardware, software, and cyber-physical attacks on embedded systems. .
650		0	_aEmbedded computer systems.
650		0	_aElectronic circuit design.
650		0	_aMicroprocessors.
650		0	_aComputer architecture.
650	1	4	_aEmbedded Systems.
650	2	4	_aElectronics Design and Verification.
650	2	4	_aProcessor Architectures.
653		0	_aEmbedded computer systems -- Security measures
653		0	_aMachine learning
700	1		_aHalak, Basel. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-94178-9 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK