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<p>A secure and efficient routing mechanism needs to be established for the management of Vehicular Ad-Hoc Network (VANET), as there is always a great level of mobility, dynamic topology, and vulnerability to security threats in such networks. Stable selection of Cluster Head (CH) and effective routing mechanism remain major problems in such environments. There is still lack of a framework which can provide a solution to all of these problems simultaneously, which can be energy efficient, provide high levels of link stability, and offer security at the same time without compromising on the packet delivery rate. The proposed Binary Cat Swarm Dependent Markov Chain Optimization (BCS-DMCO) approach makes use of Binary Cat Swarm Optimization (BCSO) for choosing the optimal CH and dependent Markov chain optimization for future link states prediction. The proposed method also makes use of CA based clustering along with trust-aware fitness functions to make better decisions. AES is also used for encryption to provide security. Simulations have been made using python. VEREMI data set (Kaggle) which contains more than 11,000 entries is utilized for intrusion detection which involves Sybil attack, replay attack, and black hole attack. The results obtained by the model include PDR of 97.84%, coverage of 95.22%, and energy balancing index of 0.80%. BCS-DMCO improves delivery ratio by around 3–6% and energy efficiency by 4–8%.</p>

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Secure Routing and Attack Detection in VANETs Using BCS-DMCO Optimization

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Muhammadu Sathik Raja,
  • Elangovan Muniyandy,
  • Krovvidi S. B. Ambika,
  • P. Rajeswari,
  • Harshini Gadam,
  • Avijit Mondal,
  • Mohan Kumar

摘要

A secure and efficient routing mechanism needs to be established for the management of Vehicular Ad-Hoc Network (VANET), as there is always a great level of mobility, dynamic topology, and vulnerability to security threats in such networks. Stable selection of Cluster Head (CH) and effective routing mechanism remain major problems in such environments. There is still lack of a framework which can provide a solution to all of these problems simultaneously, which can be energy efficient, provide high levels of link stability, and offer security at the same time without compromising on the packet delivery rate. The proposed Binary Cat Swarm Dependent Markov Chain Optimization (BCS-DMCO) approach makes use of Binary Cat Swarm Optimization (BCSO) for choosing the optimal CH and dependent Markov chain optimization for future link states prediction. The proposed method also makes use of CA based clustering along with trust-aware fitness functions to make better decisions. AES is also used for encryption to provide security. Simulations have been made using python. VEREMI data set (Kaggle) which contains more than 11,000 entries is utilized for intrusion detection which involves Sybil attack, replay attack, and black hole attack. The results obtained by the model include PDR of 97.84%, coverage of 95.22%, and energy balancing index of 0.80%. BCS-DMCO improves delivery ratio by around 3–6% and energy efficiency by 4–8%.