MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03351nam a22005175i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
TR-AnTOB |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20231121102934.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220422s2022 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9783030941789 |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-3-030-94178-9 |
| Source of number or code |
doi |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
TR-AnTOB |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
TR-AnTOB |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
İngilizce |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
TK7895.E42 |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UKM |
| Source |
bicssc |
|
|---|
| Subject category code |
TEC008010 |
| Source |
bisacsh |
|
|---|
| Subject category code |
UKM |
| Source |
thema |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
TK7895.E42EBK |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine Learning for Embedded System Security |
| Medium |
[electronic resource] / |
| Statement of responsibility, etc. |
edited by Basel Halak. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. 2022. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Cham : |
| Name of producer, publisher, distributor, manufacturer |
Springer International Publishing : |
| -- |
Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Embedded computer systems. |
|
|---|
| Topical term or geographic name entry element |
Electronic circuit design. |
|
|---|
| Topical term or geographic name entry element |
Microprocessors. |
|
|---|
| Topical term or geographic name entry element |
Computer architecture. |
|
|---|
| Topical term or geographic name entry element |
Embedded Systems. |
|
|---|
| Topical term or geographic name entry element |
Electronics Design and Verification. |
|
|---|
| Topical term or geographic name entry element |
Processor Architectures. |
| 653 #0 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Embedded computer systems -- Security measures |
|
|---|
| Uncontrolled term |
Machine learning |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Halak, Basel. |
| Relator term |
editor. |
| Relator code |
edt |
| -- |
http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://doi.org/10.1007/978-3-030-94178-9">https://doi.org/10.1007/978-3-030-94178-9</a> |
| Materials specified |
Springer eBooks |
| Public note |
Online access link to the resource |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
E-Book |
