Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids

• URL: http://arxiv.org/abs/2405.13965v1
• Date: Wed, 22 May 2024 20:04:52 GMT
• Title: Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids
• Authors: Hanyu Zeng, Pengfei Zhou, Xin Lou, Zhen Wei Ng, David K. Y. Yau, Marianne Winslett,
• Abstract summary: We propose a self-supervised learning-based framework to detect and identify various types of cyber attacks. The proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild. Experiment results in a 5-area power grid system with 37 buses demonstrate the superior performance of our framework over existing approaches.
• Score: 6.5023425872686085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes with an inevitable side effect that makes the power system more vulnerable to cyber attacks. In this paper, we propose a self-supervised learning-based framework to detect and identify various types of cyber attacks. Different from existing approaches, the proposed framework does not rely on large amounts of well-curated labeled data but makes use of the massive unlabeled data in the wild which are easily accessible. Specifically, the proposed framework adopts the BERT model from the natural language processing domain and learns generalizable and effective representations from the unlabeled sensing data, which capture the distinctive patterns of different attacks. Using the learned representations, together with a very small amount of labeled data, we can train a task-specific classifier to detect various types of cyber attacks. Meanwhile, real-world training datasets are usually imbalanced, i.e., there are only a limited number of data samples containing attacks. In order to cope with such data imbalance, we propose a new loss function, separate mean error (SME), which pays equal attention to the large and small categories to better train the model. Experiment results in a 5-area power grid system with 37 buses demonstrate the superior performance of our framework over existing approaches, especially when a very limited portion of labeled data are available, e.g., as low as 0.002\%. We believe such a framework can be easily adopted to detect a variety of cyber attacks in other power grid scenarios.

Related papers

• An Approach to Abstract Multi-stage Cyberattack Data Generation for ML-Based IDS in Smart Grids [2.5655761752240505]
  We propose a method to generate synthetic data using a graph-based approach for training machine learning models in smart grids. We use an abstract form of multi-stage cyberattacks defined via graph formulations and simulate the propagation behavior of attacks in the network.
  arXiv  Detail & Related papers  (2023-12-21T11:07:51Z)
• Everything Perturbed All at Once: Enabling Differentiable Graph Attacks [61.61327182050706]
  Graph neural networks (GNNs) have been shown to be vulnerable to adversarial attacks. We propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint.
  arXiv  Detail & Related papers  (2023-08-29T20:14:42Z)
• Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids [5.705281336771011]
  False data injection attack (FDIA) is one of the classes of attacks that target the smart measurement devices by injecting malicious data. We propose a federated learning-based scheme combined with a hybrid deep neural network architecture. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.
  arXiv  Detail & Related papers  (2023-06-17T20:29:55Z)
• 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)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples [128.25509832644025]
  There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. We present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations.
  arXiv  Detail & Related papers  (2022-12-31T04:26:25Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems [4.657875410615595]
  This paper deals with the event and intrusion detection problem by leveraging a stream data mining classifier. We first build a dictionary by learning higher-level features from unlabeled data. Then, the labeled data are represented as sparse linear combinations of learned dictionary atoms. We capitalize on those sparse codes to train the online classifier along with efficient change detectors.
  arXiv  Detail & Related papers  (2021-08-24T23:17:58Z)
• Gradient-based Data Subversion Attack Against Binary Classifiers [9.414651358362391]
  In this work, we focus on label contamination attack in which an attacker poisons the labels of data to compromise the functionality of the system. We exploit the gradients of a differentiable convex loss function with respect to the predicted label as a warm-start and formulate different strategies to find a set of data instances to contaminate. Our experiments show that the proposed approach outperforms the baselines and is computationally efficient.
  arXiv  Detail & Related papers  (2021-05-31T09:04:32Z)
• 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)
• Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions. We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
  arXiv  Detail & Related papers  (2020-06-13T08:24:33Z)

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.