Securing IoT-Cloud Applications with AQ-KGMO-DMG Enhanced SVM for Intrusion Detection

In contemporary society, the Internet has evolved into an indispensable facet of daily life, serving myriad functions across various domains. Intrusion detection, as a cornerstone of information security, plays a pivotal role in fortifying networks against potential threats, emphasizing the necessity for robust and reliable methods capable of discerning and mitigating network vulnerabilities effectively. In this work, a pioneering network intrusion detection model is introduced, leveraging Adaptive Quantum-Inspired KGMO with Dynamic Molecular Grouping (AQ-KGMO-DMG) for feature selection and employing Simplified Support Vector Machines (SVM) for the classification of intrusion data. The utilization of the UNSW-NB15 dataset serves as the litmus test for evaluating the efficacy of the developed intrusion detection model. Notably, this approach enhances the accuracy in categorizing classes with minimal instances while concurrently mitigating the false alarm rate (FAR). A notable innovation in this methodology involves the transformation of raw traffic vector data into a visual representation, thereby reducing computational costs significantly. To reduce the computation cost further, the raw traffic vector data is converted into picture format. The experimental findings showed that the proposed model performed better than conventional techniques in terms of FAR, accuracy, and computation cost.