Securing IoT networks: A robust intrusion detection system leveraging feature selection and LGBM

Intrusion Detection System (IDS) is designed being help and safeguard IoT networks from potential threats. Distributed Denial of Service (DDoS) assaults are a pernicious kind of cyber-attacks causing server disruptions in modern cyber-security world. Detecting unauthorized and suspicious activities by observing data traffic flows is crucial for enhanced network security. So, this research paper proposes an innovative solution to IoT environment by designing effective intrusion detection module. It benefits from the working principle of different modules that are operated for data dimensionality reduction, feature optimization and deep classification to maximize network security by identifying normal and malicious traffic flows. The detection process commences with data pre-processing steps such as null set removal and redundant feature elimination that provide a clear and concise representation of the data. Next, we employ the Random Subset Feature Selection (RSFS) technique to minimize dimension of preprocessed information by eliminating duplicate or redundant features. The selected feature subsets are then used as the initial search space for the Mutation Boosted Golden Jackal Optimization (MBGJO) algorithm. It helps to predict optimal attributes that contribute most effectively to detection of different attack classes. Finally, the Light Gradient Boosting Machine (LGBM) algorithm is used to train the ideal feature set and detect various attack classes in the CIC-DDoS2019 dataset. By employing this algorithm, we ensure that our detection system remains scalable and capable of handling diverse attack scenarios. Experimental findings demonstrate that our IDS attains an impressive accuracy of 99.7%. Moreover, it surpasses other state-of-the-art mechanisms with regard to scalability and security. Our intrusion detection system thus provides an effective solution for safeguarding IoT networks against potential threats.