@article{(Open Science Index):https://publications.waset.org/pdf/10012819,
	  title     = {Artificial Intelligence in Penetration Testing of a Connected and Autonomous Vehicle Network},
	  author    = {Phillip Garrad and  Saritha Unnikrishnan},
	  country	= {},
	  institution	= {},
	  abstract     = {The increase in connected and autonomous vehicles (CAV) creates more opportunities for cyber-attacks. Cyber-attacks can be performed with malicious intent or for research and testing purposes. As connected vehicles approach full autonomy, the possible impact of these cyber-attacks also grows. This review analyses the challenges faced in CAV cybersecurity testing. This includes access and cost of the representative test setup and lack of experts in the field A review of potential solutions to overcome these challenges is presented. Studies have demonstrated Artificial Intelligence (AI) as a promising technique to reduce runtime, enhance effectiveness and comprehensively cover all the standard test aspects in penetration testing in other industries. However, this review has identified a significant gap in the systematic implementation of AI for penetration testing in the CAV cybersecurity domain. The expectation from this review is to investigate potential AI algorithms, which can demonstrate similar improvements in runtime and efficiency for a CAV model. If proven to be an effective means of penetration test for CAV, this methodology may be used on a full CAV test network.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {16},
	  number    = {12},
	  year      = {2022},
	  pages     = {341 - 346},
	  ee        = {https://publications.waset.org/pdf/10012819},
	  url   	= {https://publications.waset.org/vol/192},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 192, 2022},
	}