Aug 2026· Scientific Reports· Vol 16· 0 citations· 58 references
Medicine
TL;DR
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.
Abstract
The Internet of Things (IoT) devices have grown at a very fast rate, which has led to escalated security threats. Most of the current IoT oriented lightweight intrusion detection systems do not maintain a high rate of detection performance with heterogeneous and imbalanced traffic, or the expense of increased computation and memory occurs with high detection rate. To resolve this problem, this paper presents a resource efficient IoT attack detection framework called BGL-RID (Boruta-Greedy LightGBM Resource-Efficient IoT Detection). The framework applies a hybrid feature selection pipeline that integrates both Boruta and Greedy Forward Selection (GFS) to remove unnecessary features and only include the most useful features in the pipeline. The Synthetic Minority Oversampling Technique (SMOTE) is used to deal with the issue of class imbalance. Performance is measured based on accuracy and efficiency ratio, which indicates efficiency between quality of detection and resource consumption. The performance of the proposed BGL-RID model has been tested on benchmark, edge collected and IoT specific datasets namely TONIoT, proxy-labeled Raspberry Pi, CICIDS2018, and CICIoT2023. Experimental results demonstrate strong performance across these datasets. For binary and multiclass classifications, BGL-RID attained 99.72% and 98.94% accuracy on TONIoT dataset respectively. It also attained 99.91%, 99.94%, and 98.88% accuracy on the Raspberry Pi, CICIDS2018, and the IoT-specific CICIoT2023 datasets respectively. Besides having high detection rates, the 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.
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.
H. Talabani, Zrar Khalid Abdul, Hardi Mohammed Mohammed Saleh· Cluster Computing· 0 citations
An explainable hybrid feature-selection framework (X-EFS) that combines multiple feature reduction techniques via a multi-expert system module, then uses the MDA metric to select the most important features, ensuring high performance and explainability.
Minh Trọng Hoàng, Le Thi Trang Linh, Hoang Minh Nguyen et al.· Journal of Communications So...· 0 citations
A lightweight, energy-efficient model-based intrusion detection is a security mechanism that employs deep learning (DL) methods in order to detect attacks in the network or IoT environment using minimum memory and minimum energy consumption. However, the conventional intrusion detection systems (IDS) are complex, huge in size, and consume high energy levels, which makes them inefficient for use in edge devices having small computing abilities. To overcome these limitations, a lightweight and energy-efficient intrusion detection model is proposed using a DL approach for resource-constrained edge computing environments. Initially, the input data is obtained from the CIC-IDS-2017 network intrusion dataset for validating the system performance. Also, Tail Robust Quantile Normalization (TRQN) is used to deal with outliers and skewness in order to improve the quality of data and make the system more robust. Besides that, the Mutual Information-Interaction Gain based Feature Selection (MI-IGFS) model is employed to extract the most important features, and thus decrease the model size and computational cost. For detecting intrusions, an Attention-based Multi-scale Temporal Convolutional Network (AttMT-ConNet) is developed to effectively capture temporal patterns and multi-scale dependencies in network traffic data. Incorporating attention mechanisms enhances feature representation and detection accuracy while keeping efficiency constant. The architecture implementation is carried out using the Python programming language, and performance measures are evaluated. It proves to be efficient for scalable, secure, and real-time intrusion detection in the edge computing environment.
Kiran Sankar R, Vivek Kuthanazhi, Ramya Sundaravadivelu· ITM Web of Conferences· 0 citations
The results verify the framework's ability to provide low latency and correct DDoS mitigation directly on the IoT devices, which can be considered a feasible solution to achieve resilience improvement of critical IoT deployments in health care, industrial automation, and smart cities.
Selvi T, Jayaganesh J· International journal of com...· 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 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.
Abhinay Kumar Reddy Seella, Rupesh Shirke, Vijay Kumar Kasuba et al.· International Conference on...· 0 citations
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.
P. P. Bairagi, Ashish Bagwari, Sailen Dutta Kalita et al.· international journal of eng...· 0 citations
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