Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks

• URL: http://arxiv.org/abs/2412.13240v1
• Date: Tue, 17 Dec 2024 17:40:14 GMT
• Title: Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks
• Authors: Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa,
• Abstract summary: 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.
• Score: 1.0678175996321808
• License:
• Abstract: With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. Existing research on detecting intrusions in these unbalanced labeled datasets primarily employs Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) models, which result in incomplete detection, especially for new attacks. To handle these challenges, we suggest a new approach to IoT intrusion detection using Self-Supervised Learning (SSL) with a Markov Graph Convolutional Network (MarkovGCN). Graph learning excels at modeling complex relationships within data, while SSL mitigates the issue of limited labeled data for emerging attacks. Our approach leverages the inherent structure of IoT networks to pre-train a GCN, which is then fine-tuned for the intrusion detection task. The integration of Markov chains in GCN uncovers network structures and enriches node and edge features with contextual information. Experimental results demonstrate that our approach significantly improves detection accuracy and robustness compared to conventional supervised learning methods. Using the EdgeIIoT-set dataset, we attained an accuracy of 98.68\%, a precision of 98.18%, a recall of 98.35%, and an F1-Score of 98.40%.

Related papers

• Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning [0.0]
  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.
  arXiv  Detail & Related papers  (2025-01-25T16:24:18Z)
• 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. By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks. 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)
• FedMSE: Federated learning for IoT network intrusion detection [0.0]
  The rise of IoT has expanded the cyber attack surface, making traditional centralized machine learning methods insufficient due to concerns about data availability, computational resources, transfer costs, and especially privacy preservation. A semi-supervised federated learning model was developed to overcome these issues, combining the Shrink Autoencoder and Centroid one-class classifier (SAE-CEN) This approach enhances the performance of intrusion detection by effectively representing normal network data and accurately identifying anomalies in the decentralized strategy.
  arXiv  Detail & Related papers  (2024-10-18T02:23:57Z)
• Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks [50.87615167799367]
  We certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph. Our framework provides fundamental insights into the role of graph structure and its connectivity on the worst-case behavior of convolution-based and PageRank-based GNNs.
  arXiv  Detail & Related papers  (2024-07-15T16:12:51Z)
• Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning [1.0678175996321808]
  The Internet of Things (IoT) has been introduced as a breakthrough technology that integrates intelligence into everyday objects. As the IoT networks grow and expand, they become more susceptible to cybersecurity attacks. This paper introduces a novel intrusion detection approach designed to address these challenges.
  arXiv  Detail & Related papers  (2024-06-04T06:30:22Z)
• Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction [6.374540518226326]
  Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. This paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding (PCA) for dimension reduction. Using the CIC-IDS 2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS 2018 dataset.
  arXiv  Detail & Related papers  (2024-01-22T05:49:41Z)
• 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)
• Few-shot Weakly-supervised Cybersecurity Anomaly Detection [1.179179628317559]
  We propose an enhancement to an existing few-shot weakly-supervised deep learning anomaly detection framework. This framework incorporates data augmentation, representation learning and ordinal regression. We then evaluated and showed the performance of our implemented framework on three benchmark datasets.
  arXiv  Detail & Related papers  (2023-04-15T04:37:54Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Automating Botnet Detection with Graph Neural Networks [106.24877728212546]
  Botnets are now a major source for many network attacks, such as DDoS attacks and spam. In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically.
  arXiv  Detail & Related papers  (2020-03-13T15:34:33Z)

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.