{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T06:54:57Z","timestamp":1758264897831,"version":"3.41.2"},"reference-count":43,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,3,15]]},"abstract":"<jats:p> The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles\u2019 security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks. <\/jats:p>","DOI":"10.1142\/s0218126623500585","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T08:52:09Z","timestamp":1661331129000},"source":"Crossref","is-referenced-by-count":4,"title":["AHDNN: Attention-Enabled Hierarchical Deep Neural Network Framework for Enhancing Security of Connected and Autonomous Vehicles"],"prefix":"10.1142","volume":"32","author":[{"given":"Koyel Datta","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, 110058, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak Kumar","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, 110006, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rinky","family":"Dwivedi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, 110058, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9851-4103","authenticated-orcid":false,"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Brandon University, Brandon, Canada R7A 6A9, Canada"},{"name":"Research Centre for Interneural Computing, China Medical University, Taichung 40402, Taiwan"},{"name":"Department of Computer Science and Math, Lebanese American University, Beirut 1102, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"S0218126623500585BIB001","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1049\/iet-com.2009.0191","volume":"4","author":"Tellez J.","year":"2010","journal-title":"IET Commun."},{"key":"S0218126623500585BIB002","first-page":"145","volume-title":"Proc. 11th Int. 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