Demystifying Intrusion Detection Process using Machine Learning Techniques

Machine learning methodologies have become indispensable in augmenting the efficacy of intrusion detection systems (IDS). This paper furnishes a comprehensive survey of machine learning-driven IDS strategies, encompassing diverse classification algorithms, feature selection methodologies, and anomaly detection techniques. The study scrutinizes the utilization of singular, amalgamated, and ensemble classifiers within the machine learning paradigm for intrusion detection purposes. Moreover, it investigates innovative amalgamated methodologies integrating Genetic Algorithm (GA), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), Discrete Wavelet Transform (DWT), and Support Vector Machine (SVM) to enhance intrusion detection accuracy. Additionally, the paper investigates the importance of kernel methods in intrusion identification, introducing a novel array of kernels tailored for anomaly detection. Lastly, it addresses the challenges associated with the extensive deployment of anomaly-based intrusion detectors. This research offers valuable insights into contemporary machine learning techniques for intrusion detection, providing a roadmap for researchers and practitioners in developing and deploying effective IDS solutions.