Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning

• URL: http://arxiv.org/abs/2501.15266v1
• Date: Sat, 25 Jan 2025 16:24:18 GMT
• Title: Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning
• Authors: Tasnimul Hasan, Abrar Hossain, Mufakir Qamar Ansari, Talha Hussain Syed,
• Abstract summary: Intrusion Detection Systems (IDS) are essential for identifying and preventing abnormal network behaviors and malicious activities. This research implements six innovative approaches to enhance IDS performance, including leveraging an autoencoder for dimensional reduction. We are the first to deploy our model on a Jetson Nano, achieving inference times of 0.185 ms for binary classification and 0.187 ms for multiclass classification.
• Score: 0.0
• License:
• Abstract: The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened the risk of cyberattacks, necessitating robust security measures to protect IIoT networks. Intrusion Detection Systems (IDS) are essential for identifying and preventing abnormal network behaviors and malicious activities. Despite the potential of Machine Learning (ML)--based IDS solutions, existing models often face challenges with class imbalance and multiclass IIoT datasets, resulting in reduced detection accuracy. This research directly addresses these challenges by implementing six innovative approaches to enhance IDS performance, including leveraging an autoencoder for dimensional reduction, which improves feature learning and overall detection accuracy. Our proposed Decision Tree model achieved an exceptional F1 score and accuracy of 99.94% on the Edge-IIoTset dataset. Furthermore, we prioritized lightweight model design, ensuring deployability on resource-constrained edge devices. Notably, we are the first to deploy our model on a Jetson Nano, achieving inference times of 0.185 ms for binary classification and 0.187 ms for multiclass classification. These results highlight the novelty and robustness of our approach, offering a practical and efficient solution to the challenges posed by imbalanced and multiclass IIoT datasets, thereby enhancing the detection and prevention of network intrusions.

Related papers

• Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks [1.0678175996321808]
  New types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. We suggest a new approach to IoT intrusion detection using Self-Supervised Learning (SSL) with a Markov Graph Convolutional Network (MarkovGCN) Our approach leverages the inherent structure of IoT networks to pre-train a GCN, which is then fine-tuned for the intrusion detection task.
  arXiv  Detail & Related papers  (2024-12-17T17:40:14Z)
• 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)
• Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness [47.9744734181236]
  We explore the concept of Lipschitz continuity to certify the robustness of deep neural networks (DNNs) against adversarial attacks. We propose a novel algorithm that remaps the input domain into a constrained range, reducing the Lipschitz constant and potentially enhancing robustness. Our method achieves the best robust accuracy for CIFAR10, CIFAR100, and ImageNet datasets on the RobustBench leaderboard.
  arXiv  Detail & Related papers  (2024-06-28T03:10:36Z)
• Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems [0.23408308015481666]
  Our proposed model consists on a combination of convolutional neural network (CNN) and long short-term memory (LSTM) deep learning (DL) models. This fusion facilitates the detection and classification of IoT traffic into binary categories, benign and malicious activities. Our proposed model achieves an accuracy rate of 98.42%, accompanied by a minimal loss of 0.0275.
  arXiv  Detail & Related papers  (2024-05-28T22:12:15Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• 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)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Robust Attack Detection Approach for IIoT Using Ensemble Classifier [0.0]
  The objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. The proposed model is tested on standard IoT attack outliers such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.
  arXiv  Detail & Related papers  (2021-01-30T07:21:44Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• A cognitive based Intrusion detection system [0.0]
  Intrusion detection is one of the important mechanisms that provide computer networks security. This paper proposes a new approach based on Deep Neural Network ans Support vector machine classifier. The proposed model predicts the attacks with better accuracy for intrusion detection rather similar methods.
  arXiv  Detail & Related papers  (2020-05-19T13:30:30Z)

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.