Defending Against Adversarial Attacks in Transmission- and Distribution-level PMU Data

• URL: http://arxiv.org/abs/2008.09153v1
• Date: Thu, 20 Aug 2020 18:44:37 GMT
• Title: Defending Against Adversarial Attacks in Transmission- and Distribution-level PMU Data
• Authors: Jun Jiang and Xuan Liu and Scott Wallace and Eduardo Cotilla-Sanchez and Robert Bass and Xinghui Zhao
• Abstract summary: Phasor measurement units (PMUs) provide high-fidelity data that improve situation awareness of electric power grid operations. As PMU data become more available and increasingly reliable, these devices are found in new roles within control systems. We present a comprehensive analysis of multiple machine learning techniques to detect malicious data injection within PMU data streams.
• Score: 2.5365237338254816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Phasor measurement units (PMUs) provide high-fidelity data that improve situation awareness of electric power grid operations. PMU datastreams inform wide-area state estimation, monitor area control error, and facilitate event detection in real time. As PMU data become more available and increasingly reliable, these devices are found in new roles within control systems, such as remedial action schemes and early warning detection systems. As with other cyber physical systems, maintaining data integrity and security pose a significant challenge for power system operators. In this paper, we present a comprehensive analysis of multiple machine learning techniques to detect malicious data injection within PMU data streams. The two datasets used in this study come from two PMU networks: an inter-university, research-grade distribution network spanning three institutions in the U.S. Pacific Northwest, and a utility transmission network from the Bonneville Power Administration. We implement the detection algorithms with TensorFlow, an open-source software library for machine learning, and the results demonstrate potential for distributing the training workload and achieving higher performance, while maintaining effectiveness in the detection of spoofed data.

Related papers

• Distributing Intelligence in 6G Programmable Data Planes for Effective In-Network Deployment of an Active Intrusion Detection System [2.563180814294141]
  This research aims to propose a disruptive paradigm in which devices in a typical data plane of a future programmable network have %classification and anomaly detection capabilities and cooperate in a fully distributed fashion to act as an ML-enabled Active Intrusion Detection System "embedded" into the network. The reported proof-of-concept experiments demonstrate that the proposed paradigm allows working effectively and with a good level of precision while occupying overall less CPU and RAM resources of the devices involved.
  arXiv  Detail & Related papers  (2024-10-31T15:14:15Z)
• Enhancing Automata Learning with Statistical Machine Learning: A Network Security Case Study [4.2751988244805466]
  In this paper, we use automata learning to derive state machines from network-traffic data. We apply our approach to a commercial network intrusion detection system developed by our industry partner, RabbitRun Technologies. Our approach results in an average 67.5% reduction in the number of states and transitions of the learned state machines.
  arXiv  Detail & Related papers  (2024-05-18T02:10:41Z)
• Grid Monitoring and Protection with Continuous Point-on-Wave Measurements and Generative AI [47.19756484695248]
  This article presents a case for a next-generation grid monitoring and control system, leveraging recent advances in generative artificial intelligence (AI) and machine learning. We argue for a monitoring and control framework based on the streaming of continuous point-on-wave (CPOW) measurements with AI-powered data compression and fault detection.
  arXiv  Detail & Related papers  (2024-03-11T17:28:46Z)
• 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)
• Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi Systems [9.087163485833054]
  We propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals. Our system named attention-enhanced deep learning for presence detection (ALPD) employs an attention mechanism to automatically select informative subcarriers from the CSI data. We evaluate the proposed ALPD system by deploying a pair of WiFi access points (APs) for collecting CSI dataset, which is further compared with several benchmarks.
  arXiv  Detail & Related papers  (2023-04-25T19:17:36Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT [82.33080550378068]
  The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
  arXiv  Detail & Related papers  (2022-02-03T07:12:36Z)
• Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT [55.62157530259969]
  We study a sensor's transmit power minimization subject to the peak-AoI requirement and a probabilistic constraint on queuing latency. We propose a local-model selection approach which accounts for correlation among the sensor's training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay's tail distribution.
  arXiv  Detail & Related papers  (2021-08-17T08:38:31Z)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)
• Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer [42.30741737568212]
  We propose the solution of powering devices using wireless power transfer (WPT) This work aims at the derivation of guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system. The results provide useful guidelines on WPT provisioning to provide a guaranteer on learning performance.
  arXiv  Detail & Related papers  (2021-02-24T15:47:34Z)
• Multi-Source Data Fusion for Cyberattack Detection in Power Systems [1.8914160585516038]
  We show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle attacks.
  arXiv  Detail & Related papers  (2021-01-18T06:34:45Z)

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.