Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models
Abstract Intelligently connected automobiles have come a long way thanks to the deep integration of cutting-edge networked gadgets and advancements in automotive technology. The increasing connectivity of vehicles has introduced significant cybersecurity concerns, particularly in controller area net...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00782-y |
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| author | W. Beniel Dennyson C. Jothikumar |
| author_facet | W. Beniel Dennyson C. Jothikumar |
| author_sort | W. Beniel Dennyson |
| collection | DOAJ |
| description | Abstract Intelligently connected automobiles have come a long way thanks to the deep integration of cutting-edge networked gadgets and advancements in automotive technology. The increasing connectivity of vehicles has introduced significant cybersecurity concerns, particularly in controller area network (CAN) bus systems, which are crucial for vehicle communication. For this, the research presents a novel hybrid model combining and integrating a long short-term memory (LSTM) neural network and bacterial foraging optimization (BFO) technique to address challenges like identifying fuzzy and denial of service (DoS) attacks on the CAN bus system. The model’s efficacy is demonstrated by evaluating various cyber-attacks from the Car-Hacking dataset. An adaptive feature selection method using BFO to identify optimal CAN bus characteristics for accurate attack detection. The LSTM-based temporal pattern recognition system detects anomalous message sequences and real-time countermeasures for mitigating DoS and fuzzy attacks. Reducing attack detection time to 0.0838 s, an enhancement over LSTM-AE, suggests improving detection speed. With a higher precision of 94.6% and F1-scores of 95.8%, LSTM-BFO outperforms other models based on accuracy, precision, and F1-score under the current experimental setup. |
| format | Article |
| id | doaj-art-ba8babdab8bd45e5aa6d78f12e8a235f |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-ba8babdab8bd45e5aa6d78f12e8a235f2025-08-20T03:52:23ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-04-0118112210.1007/s44196-025-00782-ySecuring Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM ModelsW. Beniel Dennyson0C. Jothikumar1Department of Computing Technologies, SRM Institute of Science and TechnologyDepartment of Computing Technologies, SRM Institute of Science and TechnologyAbstract Intelligently connected automobiles have come a long way thanks to the deep integration of cutting-edge networked gadgets and advancements in automotive technology. The increasing connectivity of vehicles has introduced significant cybersecurity concerns, particularly in controller area network (CAN) bus systems, which are crucial for vehicle communication. For this, the research presents a novel hybrid model combining and integrating a long short-term memory (LSTM) neural network and bacterial foraging optimization (BFO) technique to address challenges like identifying fuzzy and denial of service (DoS) attacks on the CAN bus system. The model’s efficacy is demonstrated by evaluating various cyber-attacks from the Car-Hacking dataset. An adaptive feature selection method using BFO to identify optimal CAN bus characteristics for accurate attack detection. The LSTM-based temporal pattern recognition system detects anomalous message sequences and real-time countermeasures for mitigating DoS and fuzzy attacks. Reducing attack detection time to 0.0838 s, an enhancement over LSTM-AE, suggests improving detection speed. With a higher precision of 94.6% and F1-scores of 95.8%, LSTM-BFO outperforms other models based on accuracy, precision, and F1-score under the current experimental setup.https://doi.org/10.1007/s44196-025-00782-yController area networkDeep learningDenial-of-serviceFuzzy attackAutomotive networkBacterial foraging optimization |
| spellingShingle | W. Beniel Dennyson C. Jothikumar Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models International Journal of Computational Intelligence Systems Controller area network Deep learning Denial-of-service Fuzzy attack Automotive network Bacterial foraging optimization |
| title | Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models |
| title_full | Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models |
| title_fullStr | Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models |
| title_full_unstemmed | Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models |
| title_short | Securing Automotive Networks from DoS and Fuzzy Attacks with Optimized LSTM Models |
| title_sort | securing automotive networks from dos and fuzzy attacks with optimized lstm models |
| topic | Controller area network Deep learning Denial-of-service Fuzzy attack Automotive network Bacterial foraging optimization |
| url | https://doi.org/10.1007/s44196-025-00782-y |
| work_keys_str_mv | AT wbenieldennyson securingautomotivenetworksfromdosandfuzzyattackswithoptimizedlstmmodels AT cjothikumar securingautomotivenetworksfromdosandfuzzyattackswithoptimizedlstmmodels |