The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.

The existing Learning Management Systems (LMSs) are profoundly effective in empowering the organization of e-learning, however, lacking in usability and learnability. The complex navigation and an immature search system are catalysing the issues that needs vigorous improvement. This paper aims to enhance the usability of LMSs by introducing an intelligent multi-agent based voice-enabled virtual assistant, named Scavenge. The Scavenge can work with LMS to speed-up user tasks through natural voice-command (or text). It can perform many internal and external actions without a mouse or keyboard clicks, and capable to provide the results of user search queries. A prototype system was developed with Moodle LMS and conducted a usability test based on ISO/IEC 9126-4 usability metrics that are effectiveness, efficiency, and satisfaction. A group of University students were selected, who were involved in the usability testing of the proposed system. The results have shown that the proposed system has a positive impact on students’ performance and can enhance the usability of LMS. The approach and techniques used in Scavenge are novel and innovative that will be a valuable contribution to the overall field of knowledge.