Aug 2026· Cluster Computing· Vol 29· 0 citations· 53 references
Computer Science
TL;DR
This study proposes a feature selection approach based on Ant Colony Optimization (ACO) to identify the most relevant features for anomaly-based intrusion detection system (IDS) and reduces the feature set to 10 from the original datasets while achieving 100% detection accuracy and minimal training and detection times.
Besides having high detection rates, the proposed BGL-RID model also achieves the highest efficiency ratio across different datasets, showing that it is robust and scalable, with minimal computation and memory requirements, suggesting its potential suitability for resource-constrained IoT applications.
Mohd Zain Khan, Mahfooz Alam, Irfan Alam et al.· Scientific Reports· 0 citations
This study investigates the effectiveness of supervised machine learning techniques for detecting cyberattacks in IoT-based smart city networks using the TON_IoT dataset, finding that advanced ensemble learning combined with robust feature engineering provides a reliable and scalable solution for securing smart city Io...
E. Okonta, Oluwaseun Bamgbose· ABC2: Journal of Architectur...· 0 citations
A novel feature selection framework based on the Tunicate Swarm Algorithm to enhance intrusion detection in IoT networks and pave the way for the Tunicate Swarm Algorithm, a novel feature selection paradigm that can be well-suited to scalability and deployability in IDS on IoT networks.
Mohamed Guendouz, Hadj Ahmed Bouarara, M. Rahmani· Cluster Computing· 0 citations
The majority of assaults in heterogeneous networks are detected by intrusion detection systems (IDS). Cyberattack kinds that seriously harm networks are difficult for conventional IDSs to detect. The majority of existing solutions rely on deep learning models, which have a significant computational and energy overhead...
Abhinay Kumar Reddy Seella, Rupesh Shirke, Vijay Kumar Kasuba et al.· International Conference on...· 0 citations
This paper proposes a hybrid IDS framework that integrates three key components, such as the novel exponential grey wolf-optimized grid search algorithm (EGWOGSA), which is a stochastic optimization algorithm, while feature selection is achieved using the novel symmetric gradient Boruta for enhanced feature selection a...
Joseph Kipongo, Theo G. Swart, Ebenezer Esenogho· Soft Computing - A Fusion of...· 0 citations
The results of this study show that the optimization process not only greatly improves the classification accuracy but also saves a lot of time in computations in the detection of varied IoT attacks.
Mayank Agarwal· Journal of Intelligent Decis...· 0 citations
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