Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks

• URL: http://arxiv.org/abs/2210.07707v1
• Date: Fri, 14 Oct 2022 11:20:08 GMT
• Title: Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks
• Authors: Liu Yang, Simon X. Yang, Yun Li, Yinzhi Lu, Tan Guo
• Abstract summary: This paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs) It achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.
• Score: 11.56611183738877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional machine learning techniques have been widely used to establish the trust management systems. However, the scale of training dataset can significantly affect the security performances of the systems, while it is a great challenge to detect malicious nodes due to the absence of labeled data regarding novel attacks. To address this issue, this paper presents a generative adversarial network (GAN) based trust management mechanism for Industrial Wireless Sensor Networks (IWSNs). First, type-2 fuzzy logic is adopted to evaluate the reputation of sensor nodes while alleviating the uncertainty problem. Then, trust vectors are collected to train a GAN-based codec structure, which is used for further malicious node detection. Moreover, to avoid normal nodes being isolated from the network permanently due to error detections, a GAN-based trust redemption model is constructed to enhance the resilience of trust management. Based on the latest detection results, a trust model update method is developed to adapt to the dynamic industrial environment. The proposed trust management mechanism is finally applied to secure clustering for reliable and real-time data transmission, and simulation results show that it achieves a high detection rate up to 96%, as well as a low false positive rate below 8%.

Related papers

• Sdn Intrusion Detection Using Machine Learning Method [0.0]
  Software-defined network (SDN) is a new approach that allows network control to become directly programmable. This research developed a novel machine-learning method to capture infections in networks.
  arXiv  Detail & Related papers  (2024-11-08T12:19:50Z)
• Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space [7.115540429006041]
  This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applying to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities.
  arXiv  Detail & Related papers  (2024-09-19T08:09:44Z)
• TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support [59.41529066449414]
  We propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity. TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Experiments show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot.
  arXiv  Detail & Related papers  (2023-06-23T07:39:12Z)
• KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks [63.531790269009704]
  Social Internet of Things (SIoT) is a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things) Due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. We propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT.
  arXiv  Detail & Related papers  (2023-02-22T14:24:45Z)
• An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks [8.611020067829509]
  A fuzzy trust evaluation method is presented to transform the transmission evidences into trust values. A K-Means based outlier detection scheme is proposed to further analyze plenty of trust values. An evolutionary game based secure clustering protocol is presented to achieve a trade-off between security assurance and energy saving.
  arXiv  Detail & Related papers  (2022-07-21T03:24:35Z)
• A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks [9.238298040561173]
  This paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection. Experiments verify that our protocol can effectively defend the network against attacks from internal malicious or compromised nodes.
  arXiv  Detail & Related papers  (2022-07-20T14:23:29Z)
• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• Provably Robust Detection of Out-of-distribution Data (almost) for free [124.14121487542613]
  Deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. In this paper we propose a novel method where from first principles we combine a certifiable OOD detector with a standard classifier into an OOD aware classifier. In this way we achieve the best of two worlds: certifiably adversarially robust OOD detection, even for OOD samples close to the in-distribution, without loss in prediction accuracy and close to state-of-the-art OOD detection performance for non-manipulated OOD data.
  arXiv  Detail & Related papers  (2021-06-08T11:40:49Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)
• 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)

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.