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020 ▼a 9781003610908 ▼q (electronic bk.)
020 ▼a 1003610900 ▼q (electronic bk.)
020 ▼a 9781040358696 ▼q (electronic bk. : PDF)
020 ▼a 1040358691 ▼q (electronic bk. : PDF)
020 ▼a 9781040358702 ▼q (electronic bk. : EPUB)
020 ▼a 1040358705 ▼q (electronic bk. : EPUB)
020 ▼z 9781041006404
0247 ▼a 10.1201/9781003610908 ▼2 doi
037 ▼a 9781003610908 ▼b Taylor & Francis
040 ▼a EBZ ▼b eng ▼c EBZ ▼d 248032
049 ▼a MAIN
050 4 ▼a QA76.9.A25
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08204 ▼a 005.8 ▼2 23/eng/20250324
1001 ▼a Alruwaili, Ahmed, ▼e author.
24510 ▼a Cybersecurity in Robotic Autonomous Vehicles : ▼b Machine Learning Applications to Detect Cyber Attacks / ▼c Ahmed Alruwaili, Sardar M. N. Islam, Iqbal Gondal.
250 ▼a First edition.
260 ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c 2025.
300 ▼a 1 online resource (106 pages) : ▼b illustrations.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
3410 ▼a textual ▼2 sapdv ▼3 EBSCOhost
5050 ▼a 1. Introduction. 2. Theoretical Lens. 3. Exploring CAN Bus Security: Insights and Analysis. 4. Research Design. 5. Results and Discussion. 6. Conclusions and Future Research.
520 ▼a Cybersecurity in Robotic Autonomous Vehicles introduces a novel intrusion detection system (IDS) specifically designed for AVs, which leverages data prioritisation in CAN IDs to enhance threat detection and mitigation. It offers a pioneering intrusion detection model for AVs that uses machine and deep learning algorithms.Presenting a new method for improving vehicle security, the book demonstrates how the IDS has incorporated machine learning and deep learning frameworks to analyse CAN bus traffic and identify the presence of any malicious activities in real time with high level of accuracy. It provides a comprehensive examination of the cybersecurity risks faced by AVs with a particular emphasis on CAN vulnerabilities and the innovative use of data prioritisation within CAN IDs.The book will interest researchers and advanced undergraduate students taking courses in cybersecurity, automotive engineering, and data science. Automotive industry and robotics professionals focusing on Internet of Vehicles and cybersecurity will also benefit from the contents.
532 0 ▼3 EBSCOhost ▼a "EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025.
5880 ▼a Online resource; title from PDF title page (Taylor & Francis, viewed March 24, 2025).
590 ▼a OCLC control number change
650 0 ▼a Computer security.
650 0 ▼a Automated vehicles.
653 ▼a Internet of Vehicles;autonomous vehicles;machine learning;vehicle security;threat detection models;Controller Area Network (CAN)
7001 ▼a Islam, Sardar M. N., ▼d 1950-, ▼e author. ▼1 https://id.oclc.org/worldcat/entity/E39PBJpdPGRgbHQ3fmFXq4VwG3
7001 ▼a Gondal, Iqba?l, ▼e author. ▼1 https://id.oclc.org/worldcat/entity/E39PCjGJQJp4646HQKVPfPYr4q
85640 ▼3 EBSCOhost ▼u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=4112921
938 ▼a EBSCOhost ▼b EBSC ▼n 4112921
990 ▼a 관리자