LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks

• URL: http://arxiv.org/abs/2605.02987v1
• Date: Mon, 04 May 2026 14:04:59 GMT
• Title: LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks
• Abstract summary: LiteShield is a machine learning-based intrusion detection system (IDS) for Internet of Things (IoT) environments.<n>It combines hybrid feature selection with efficient classifiers to support accurate attack detection under limited computational budgets.<n>Six lightweight classifiers are evaluated for both binary and multiclass intrusion detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid expansion of Internet of Things (IoT) deployments has enlarged the attack surface of modern digital infrastructure while exposing a key security mismatch: many intrusion detection systems (IDSs) remain too computationally expensive for constrained IoT environments. This paper presents LiteShield, a lightweight machine learning-based IDS that combines hybrid feature selection with efficient classifiers to support accurate attack detection under limited computational budgets. The proposed framework uses the UNSW-NB15 dataset, applies data preprocessing and imbalance-aware preparation, and employs a two-stage feature selection pipeline based on Mutual Information (MI) and Recursive Feature Elimination with Cross-Validation (RFECV). Six lightweight classifiers are evaluated for both binary and multiclass intrusion detection: Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Naïve Bayes, and Support Vector Machine. Experimental results show that KNN achieved the highest raw predictive performance, reaching 98.26% accuracy for binary classification and 85.22% accuracy for multiclass classification. However, Random Forest delivered the most practical trade-off between detection quality and deployment efficiency, obtaining 98.01% binary accuracy and 80.39% multiclass accuracy with substantially lower model size and inference cost than KNN. Additional ablation analysis on minority attack classes indicates that class imbalance materially affects multiclass performance. Overall, LiteShield demonstrates that hybrid feature selection and lightweight machine learning can provide a viable path toward accurate and computationally feasible intrusion detection for IoT-focused environments.

Related papers

• IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection [0.4549831511476248]
  Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD.<n>We propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling.<n>Our pipeline incorporates rigorous preprocessing, stratified data splitting, and dynamic class weighting to enhance the detection of rare classes.
  arXiv  Detail & Related papers  (2025-10-01T06:51:25Z)
• 10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection [48.074211420276605]
  Binarized neural networks (BNNs) are distinguished by their exceptional efficiency in model compression.<n>We propose the Binarized Infrared Small-Target Detection Network (BiisNet)<n>BiisNet preserves the core operations of binarized convolutions while integrating full-precision features into the network's information flow.
  arXiv  Detail & Related papers  (2025-03-04T14:25:51Z)
• Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models [0.23408308015481666]
  This study presents the development and evaluation of an advanced Intrusion detection (IDS) based on a hybrid LSTM-convolution neural network (CNN)-Attention architecture.<n>The research focuses on two key classification tasks: binary and multi-class classification.<n>In binary classification, the model achieved near-perfect accuracy, while in multi-class classification, it maintained a high accuracy level (99.04%), effectively categorizing different attack types with a loss value of 0.0220%.
  arXiv  Detail & Related papers  (2025-01-21T20:52:23Z)
• Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks [47.18575262588692]
  We propose a novel Multi-Space Prototypical Learning framework tailored for few-shot attack detection.<n>By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks.<n> Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types.
  arXiv  Detail & Related papers  (2024-12-28T00:09:46Z)
• Optimized IoT Intrusion Detection using Machine Learning Technique [0.0]
  Intrusion detection systems (IDSs) are essential for defending against a variety of attacks.<n>The functional and physical diversity of IoT IDS systems causes significant issues.<n>For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy.
  arXiv  Detail & Related papers  (2024-12-03T21:23:54Z)
• Efficient Intrusion Detection: Combining $χ^2$ Feature Selection with CNN-BiLSTM on the UNSW-NB15 Dataset [2.239394800147746]
  Intrusion Detection Systems (IDSs) have played a significant role in the detection and prevention of cyber-attacks in traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices pose a challenge for deploying conventional computing-based IDSs. We present an effective IDS model that addresses this issue by combining a lightweight Convolutional Neural Network (CNN) with bidirectional Long Short-Term Memory (BiLSTM)
  arXiv  Detail & Related papers  (2024-07-20T17:41:16Z)
• Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices [38.16309790239142]
  Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
  arXiv  Detail & Related papers  (2024-06-04T20:36:21Z)
• Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction [3.5889226512319903]
  Internet of things (IoT) devices are highly vulnerable to cyber-attacks. A variety of machine learning-based network intrusion detection methods for IoT networks have been developed. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics.
  arXiv  Detail & Related papers  (2023-07-04T08:48:01Z)
• Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS [48.353590166168686]
  Internet of Things (IoT) networks contain benign traffic far more than abnormal traffic, with some rare attacks. Most existing studies have been focused on sacrificing the detection rate of the majority class in order to improve the detection rate of the minority class. We propose a lightweight framework named S2CGAN-IDS to expand the number of minority categories in both data space and feature space.
  arXiv  Detail & Related papers  (2023-06-06T14:19:23Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)
• GIM: Gaussian Isolation Machines [40.7916016364212]
  In many cases, neural network classifiers are exposed to input data that is outside of their training distribution data. We present a novel hybrid (generative-discriminative) classifier aimed at solving the problem arising when OOD data is encountered. The proposed GIM's novelty lies in its discriminative performance and generative capabilities, a combination of characteristics not usually seen in a single classifier.
  arXiv  Detail & Related papers  (2020-02-06T09:51:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.