A Modified Possibilistic Fuzzy C Means (MPFCM) Clustering based Intrusion Detection Framework for VANETs Using Improved Whale Optimization with Enhanced Deep Neural Networks
Abstract
Vehicular Ad Hoc Networks (VANETs) operate in highly dynamic and high-velocity environments, making them especially vulnerable to a wide range of intrusions and malicious attacks. To address these challenges, this paper proposes a hybrid IDS framework that combines multiple state-of-the-art techniques tailored for VANET traffic. Initially a Z-Score Normalization ensures all features share a consistent scale, improving model convergence and preventing domination by outliers in vehicular data. Next, an Improved Whale Optimization Algorithm (IWOA) is employed to select the most discriminative feature subset by simulating whale-hunting strategies with adaptive parameters for robust global search in rapidly changing VANET conditions. Then an Improved Deep Neural Network (IDNN) functions as a signature-based classifier, rapidly identifying known intrusions through enhanced architecture, optimization, and regularization. Data classified as normal or inconclusive is then passed to a Modified Possibilistic Fuzzy C Means (MPFCM) clustering based anomaly detection module, leveraging fuzzy logic to isolate novel or zero-day threats that deviate from typical vehicle communication patterns. The framework’s performance is evaluated using accuracy, precision, recall, and f-measure, ensuring a balanced view of both detection thoroughness and correctness. Experimental results demonstrate that this integrated solution achieves high detection rates, low false alarms, and robust adaptability to evolving attack behaviors in VANETs, making it a promising approach to secure next-generation intelligent transportation systems.
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