國立高雄大學圖資館 |

登入

回首頁

Machine learning for embedded system security

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for embedded system securityedited by Basel Halak.
其他作者:	Halak, Basel.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xv, 160 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Embedded computer systemsSecurity measures.
電子資源:	https://doi.org/10.1007/978-3-030-94178-9
ISBN:	9783030941789$q(electronic bk.)

Machine learning for embedded system security
Machine learning for embedded system security[electronic resource] /edited by Basel Halak. - Cham :Springer International Publishing :2022. - xv, 160 p. :ill., digital ;24 cm.

Introduction -- 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.

This 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.

ISBN: 9783030941789$q(electronic bk.)

Standard No.: 10.1007/978-3-030-94178-9doiSubjects--Topical Terms:

485279
Embedded computer systems
--Security measures.

LC Class. No.: TK7895.E42 / M33 2022

Dewey Class. No.: 005.8

Machine learning for embedded system security
LDR:02575nmm a2200325 a 4500 001 621049
003 DE-He213
005 20220422130105.0
006 m d
007 cr nn 008maaau
008 221121s2022 sz s 0 eng d
020 $a 9783030941789$q(electronic bk.)
020 $a 9783030941772$q(paper)
024 7 $a 10.1007/978-3-030-94178-9 $2 doi
035 $a 978-3-030-94178-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7895.E42 $b M33 2022
072 7 $a UKM $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a UKM $2 thema
082 0 4 $a 005.8 $2 23
090 $a TK7895.E42 $b M149 2022
245 0 0 $a Machine learning for embedded system security $h [electronic resource] / $c edited by Basel Halak.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xv, 160 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- 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 $a This 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 $a Embedded computer systems $x Security measures. $3 485279
650 0 $a Machine learning. $3 188639
650 1 4 $a Embedded Systems. $3 919564
650 2 4 $a Electronics Design and Verification. $3 919565
650 2 4 $a Processor Architectures. $3 274498
700 1 $a Halak, Basel. $3 814908
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-94178-9
950 $a Engineering (SpringerNature-11647)