The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.

Top-down visual attention models are essential for task-driven object detection; however, many existing approaches do not effectively integrate multiple sources of high-level guidance such as scene context and target-specific information. This paper proposes a computational framework that combines contextual information and target object features to dynamically modulate low-level visual features for improved attentional selection. The proposed model consists of three key components: (i) a contextual weighting module that learns feature weights optimised using Particle Swarm Optimisation and predicted through a hetero-associative neural network; (ii) a target-aware attention module that estimates feature importance based on target-specific characteristics derived from low-level feature distributions; and (iii) a recognition module that performs region classification using a Naïve Bayes classifier. Experiments conducted on seven challenging datasets with diverse objects and cluttered backgrounds demonstrate that integrating contextual and target-specific information improves detection performance compared to using either source independently. The primary objective of this work is to investigate the complementary and synergistic effects of combining contextual information and target object knowledge within a bottom-up saliency framework. The goal is not to propose or claim a state-of-the-art top-down visual attention model, but rather to demonstrate that integrating multiple sources of guidance can enhance saliency-based target detection under varied visual conditions.