Multi-Method Stacked Feature Selection Approach based IDS for IoT Networks

Abstract

In an era marked by the proliferation of interconnected devices, the Internet of Things (IoT) has emerged as a revolutionary technological paradigm. IoT networks encompass a vast array of devices, from smart appliances to industrial sensors, revolutionizing industries and everyday life. However, this ubiquitous connectivity has ushered in a new frontier of security challenges, necessitating the deployment of robust Intrusion Detection Systems (IDS). This paper presents a pioneering Multi-Method Stacked Feature Selection (M2SFS) approach-based IDS tailored explicitly for IoT networks. By orchestrating a diverse ensemble of feature selection techniques, the M2SFS framework adeptly curates the feature set derived from the CICIDS 2017 dataset. This comprehensive selection strategy optimally reduces dimensionality, mitigating the resource constraints inherent to IoT ecosystems. The proposed IDS, rooted in the M2SFS approach, demonstrates exceptional accuracy and efficiency in detecting anomalous network behaviors. By harnessing the collective strength of stacked feature selection, this IDS capitalizes on the synergistic potential of multiple feature selection methods. The result is an IDS uniquely adapted to the intricacies of IoT networks, effectively fortifying their security posture. Through rigorous experimentation using the CICIDS 2017 dataset, the proposed approach affirms the superiority of the M2SFS-based IDS over conventional methods. This study underscores the pressing need for IDS in IoT environments and highlights the promise of our innovative approach in safeguarding IoT networks against emerging threats.