001446466 000__ 03299cam\a2200481Ii\4500
001446466 001__ 1446466
001446466 003__ OCoLC
001446466 005__ 20230310004000.0
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001446466 007__ cr\cn\nnnunnun
001446466 008__ 220506s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001446466 020__ $$a9783030941789$$q(electronic bk.)
001446466 020__ $$a3030941787$$q(electronic bk.)
001446466 020__ $$z9783030941772
001446466 020__ $$z3030941779
001446466 0247_ $$a10.1007/978-3-030-94178-9$$2doi
001446466 035__ $$aSP(OCoLC)1314280500
001446466 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001446466 049__ $$aISEA
001446466 050_4 $$aTK7895.E42
001446466 08204 $$a005.8$$223
001446466 08204 $$a006.2/2$$223/eng/20220506
001446466 24500 $$aMachine learning for embedded system security /$$cedited by Basel Halak, editor.
001446466 264_1 $$aCham :$$bSpringer,$$c2022.
001446466 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and colour).
001446466 336__ $$atext$$btxt$$2rdacontent
001446466 337__ $$acomputer$$bc$$2rdamedia
001446466 338__ $$aonline resource$$bcr$$2rdacarrier
001446466 5050_ $$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. .
001446466 506__ $$aAccess limited to authorized users.
001446466 520__ $$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. .
001446466 588__ $$aDescription based on print version record.
001446466 650_0 $$aEmbedded computer systems$$xSecurity measures.
001446466 650_0 $$aMachine learning.
001446466 655_0 $$aElectronic books.
001446466 7001_ $$aHalak, Basel,$$eeditor.
001446466 77608 $$iPrint version:$$tMachine learning for embedded system security.$$dCham : Springer, 2022$$z9783030941772$$w(OCoLC)1308631062
001446466 852__ $$bebk
001446466 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-94178-9$$zOnline Access$$91397441.1
001446466 909CO $$ooai:library.usi.edu:1446466$$pGLOBAL_SET
001446466 980__ $$aBIB
001446466 980__ $$aEBOOK
001446466 982__ $$aEbook
001446466 983__ $$aOnline
001446466 994__ $$a92$$bISE