An Effective Hierarchical Anomaly Based Intrusion Detection System for IoT Using Ant Colony Optimization Based Feature Selection

A resource efficient IoT intrusion detection model using hybrid feature selection for edge computing

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.

Machine Learning-Based Intrusion Detection for Smart City Internet of Things Networks

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 IoT networks.

TSA-FS: a tunicate swarm algorithm-based feature selection framework for intelligent intrusion detection in internet of things networks

Energy-Efficient Machine Learning (ML)-Based Intrusion Detection System (IDS) for IoT Devices

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 that limits their use in IoT environments with limited resources. A lightweight IDS based on ML is proposed in this research as a solution to this difficulty. Predicting the behavior of network traffic is achieved using ToN-IoT data and a tailored preprocessing pipeline. The voting-based ensemble classifier is built through the combination of models of RF and LightGBM to enhance the stability of the classification. The standard performance measures that are utilized to evaluate the proposed approach include accuracy, precision, recall, F1score, false alarm rates, and ROC analysis. The experimental findings indicate that RF achieve 99.81% accuracy, LGBM achieve 99.83%, and the ensemble model has a high accuracy of 99.99% with very low false alarms. Comparative evaluation with traditional ML and DL models demonstrates improved detection reliability with reduced computational overhead. These results prove that the suggested architecture is both computationally efficient and practically applicable to IoT settings with limited resources. However, direct hardware-level energy measurements are required to fully quantify the energy-saving characteristics of the proposed IDS.

Advanced intrusion detection in IoT-enabled SDWSNs using artificial intelligence and feature optimization techniques

The widespread deployment of the Internet of Things (IoT) and software-defined wireless sensor networks (SDWSNs) gave rise to new opportunities for smart environments but has also made these systems highly vulnerable to diverse cyberattacks. Conventional intrusion detection systems (IDSs) in IoT-enabled SDWSNs face major challenges such as high-dimensional data, inadequate preprocessing, feature extraction complexities, intricate feature selection, inefficient intrusion recognition, high computational cost, and real-time classification problems, which limit their performance in resource-constrained IoT networks. To address these limitations and challenges, 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 algorithm (SGBFSA), and a gated bidirectional recurrent convolutional neural network (GBR-CNN) algorithm for intrusion recognition. Extensive simulations, using the network simulator 3 (NS-3), were conducted with the NSL-KDD dataset to evaluate the framework for IoT-enabled SDWSNs. Results demonstrate that the proposed method outperforms other state-of-the-art models across most metrics, achieving 96.7% training accuracy and 91.6% testing accuracy, with 98.03% precision, 95.9% recall, and a 96.9% F1-score, and demonstrating low energy consumption and latency, high throughput, and a reliable packet delivery ratio (PDR), leading to an extended network lifetime. The study demonstrates that the proposed framework is suitable for real-time deployment. This research contributes to the advancement of security in IoT-enabled SDWSNs by proposing an efficient, accurate, and scalable IDS framework for securing them against evolving threats through advanced feature optimization and artificial intelligence techniques.

Intrusion Detection and Classification using Deep Learning and Nature Inspired Optimization for IOT Based Network

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.