Citation :

R. Raja Kumar, T. Suresh, K. Sekar, "Effective Intrusion Detection and Classification in VANET Environments Using Deep Learning Model with Optimization Algorithms," International Journal of Computer Science and Engineering, vol. 13, no. 10, pp. 14-27, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I10P102

Abstract

VANET (Vehicular Ad Hoc Network) is an innovative transportation technology that improves road safety and traffic management through communication and data sharing between vehicles. The attackers create various kinds of assaults to oppose the operational tasks of the network environment in VANET. Then, identifying the attacks in VANET method is extremely essential to give reliable and secure communication among each vehicle within the system. Because of this, the Intrusion Detection System (IDS) is needed in VANET to offer safety from outside attacks like Botnet, Denial of Service (DoS), and so on. These may identify assaults and provoke a response, lowering the effects on the target system. This paper develops an Intelligent Intrusion Detection and Classification using Deep Learning Model and Metaheuristic Optimization Algorithms (IIDC-DLMMOA) method in VANET Environments. The main intention of IIDC-DLMMOA technique relies on developing an effective structure for IDS in VANET communications. The hiking optimization algorithm (HOA) performs feature selection by identifying the input features that contribute most to the classification process. For the classification process, the proposed IIDC-DLMMOA designs a spatial-temporal attentive with long short-term memory (STA-LSTM) system. At last, the hyperparameter tuning optimizes a classification model of STA-LSTM method through a Multi-Objective Mantis Search Algorithm (MOMSA) by selecting the best parameter values. The developed IIDC-DLMMOA system is verified on the real-time benchmark database, and the stimulated outcomes are in comparison with other advanced techniques.

Keywords

VANET, Intrusion Detection, Deep Learning, Multi-Objective Mantis Search Algorithm, Feature Selection, Vehicles-To-Everything.

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