Towards Privacy-Preserving Anomaly-Based Intrusion Detection in Energy Communities

• URL: http://arxiv.org/abs/2502.19154v1
• Date: Wed, 26 Feb 2025 14:13:02 GMT
• Title: Towards Privacy-Preserving Anomaly-Based Intrusion Detection in Energy Communities
• Authors: Zeeshan Afzal, Giovanni Gaggero, Mikael Asplund,
• Abstract summary: Energy communities may increase the vulnerability of the grid to cyber threats.<n>We propose an anomaly-based intrusion detection system to enhance the security of energy communities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Energy communities consist of decentralized energy production, storage, consumption, and distribution and are gaining traction in modern power systems. However, these communities may increase the vulnerability of the grid to cyber threats. We propose an anomaly-based intrusion detection system to enhance the security of energy communities. The system leverages deep autoencoders to detect deviations from normal operational patterns in order to identify anomalies induced by malicious activities and attacks. Operational data for training and evaluation are derived from a Simulink model of an energy community. The results show that the autoencoder-based intrusion detection system achieves good detection performance across multiple attack scenarios. We also demonstrate potential for real-world application of the system by training a federated model that enables distributed intrusion detection while preserving data privacy.

Related papers

• Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning [7.540446548202259]
  inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system. We propose to employ reinforcement learning to identify potential threats and system vulnerabilities.
  arXiv  Detail & Related papers  (2024-08-30T01:09:32Z)
• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids [0.0]
  This paper proposes an unsupervised adversarial autoencoder (AAE) model to detect false data injection attacks (FDIAs) in unbalanced power distribution grids. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements. It is tested on IEEE 13-bus and 123-bus systems with historical meteorological data and historical real-world load data.
  arXiv  Detail & Related papers  (2024-03-31T01:20:01Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Investigation of Multi-stage Attack and Defense Simulation for Data Synthesis [2.479074862022315]
  This study proposes a model for generating synthetic data of multi-stage cyber attacks in the power grid. It uses attack trees to model the attacker's sequence of steps and a game-theoretic approach to incorporate the defender's actions.
  arXiv  Detail & Related papers  (2023-12-21T09:54:18Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination [1.0621485365427565]
  This paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC. We put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. To adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD.
  arXiv  Detail & Related papers  (2023-04-07T13:43:57Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks [1.5791732557395552]
  We study the vulnerabilities of deep learning-based energy theft detection through adversarial attacks, including single-step attacks and iterative attacks. The evaluation based on three types of neural networks shows that the adversarial attacker can report extremely low consumption measurements to the utility without being detected by the DL models.
  arXiv  Detail & Related papers  (2020-10-16T02:25:40Z)
• Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks [62.997667081978825]
  We present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks. A set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel. The overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives.
  arXiv  Detail & Related papers  (2020-02-11T13:58:16Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.