Volume 18, Number 3

Lightweight IDs-Based Feature Selection Algorithm for Cyber-Physical Systems & IoE Devices

  Authors

Sunil Kaushik¹, Akashdeep Bhardwaj², Saud Aljaloud³ and Naif Alsharabi³, ¹Indus Towers, India, ²Centre for Cybersecurity, India, ³University of Hail, Saudi Arabia

  Abstract

The quick spread of Internet connections has instigated the revolutionary age of Cyber-Physical Systems (CPS) and Internet of Everything (IoE) devices. The IOE and CPS devices are the cornerstone of Industry 4.0. which is centred on Machine-to-Machine (M2M) communication. IoE and CPS devices are used in hostile environments and have limited computing and energy resources. Criticality and dependence of the Internet have exposed IoE and CPS systems to cyber-attacks. Thus, to prevent any damage, these systems require a competent and lightweight intrusion detection system (IDS). The current research recommends a novel IDS built upon a new feature selection algorithm which can identify entropy reducing and highly statistical reliable features from a dataset. The proposed feature selection technique showed significant improvements in performance measures for several classifiers. Proposed IDS with the IOTID20 dataset demonstrated that the accuracy and performance metrics exceeded 99%. The trustworthiness of the proposed IDS is further supported by its constant efficacy on the NSLKDD dataset. The proposed IDS is found to be competitive with all previous studies in all performance areas. Thus, proposed IDS on novel and innovative feature selection techniques can protect the digital ecosystem and IoE landscapes from cyber-attacks to bolster Industry 4.0.

  Keywords

Smart Devices, Threat Intelligence, IoT Vulnerabilities, Intelligent Intrusion Detection, Connected Systems, Feature Selection, IoE Security.