ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents

• URL: http://arxiv.org/abs/2305.09476v1
• Date: Fri, 21 Apr 2023 11:36:18 GMT
• Title: ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents
• Authors: Thomas Wolgast, Nils Wenninghoff, Stephan Balduin, Eric Veith, Bastian Fraune, Torben Woltjen, Astrid Nie{\ss}e
• Abstract summary: ANALYSE is a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems. It is designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ongoing penetration of energy systems with information and communications technology (ICT) and the introduction of new markets increase the potential for malicious or profit-driven attacks that endanger system stability. To ensure security-of-supply, it is necessary to analyze such attacks and their underlying vulnerabilities, to develop countermeasures and improve system design. We propose ANALYSE, a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems, consisting of the power system, ICT, and energy markets. ANALYSE is a modular, configurable, and self-documenting framework designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature.

Related papers

• Countering Autonomous Cyber Threats [40.00865970939829]
  Foundation Models present dual-use concerns broadly and within the cyber domain specifically. Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations. This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
  arXiv  Detail & Related papers  (2024-10-23T22:46:44Z)
• Threat analysis and adversarial model for Smart Grids [1.7482569079741024]
  The cyber domain of this smart power grid opens a new plethora of threats. Different stakeholders including regulation bodies, industry and academy are making efforts to provide security mechanisms to mitigate and reduce cyber-risks. Recent work shows a lack of agreement among grid practitioners and academic experts on the feasibility and consequences of academic-proposed threats. This is in part due to inadequate simulation models which do not evaluate threats based on attackers full capabilities and goals.
  arXiv  Detail & Related papers  (2024-06-17T16:33:46Z)
• Strategic Deployment of Honeypots in Blockchain-based IoT Systems [1.3654846342364306]
  It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. The model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks.
  arXiv  Detail & Related papers  (2024-05-21T17:27:00Z)
• 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)
• TMAP: A Threat Modeling and Attack Path Analysis Framework for Industrial IoT Systems (A Case Study of IoM and IoP) [2.9922995594704984]
  To deploy secure Industrial Control and Production Systems (ICPS) in smart factories, cyber threats and risks must be addressed. Current approaches for threat modeling in cyber-physical systems (CPS) are ad hoc and inefficient. This paper proposes a novel quantitative threat modeling approach, aiming to identify probable attack vectors, assess the path of attacks, and evaluate the magnitude of each vector.
  arXiv  Detail & Related papers  (2023-12-23T18:32:53Z)
• ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management [65.0114141380651]
  ThreatKG is an automated system for OSCTI gathering and management. It efficiently collects a large number of OSCTI reports from multiple sources. It uses specialized AI-based techniques to extract high-quality knowledge about various threat entities.
  arXiv  Detail & Related papers  (2022-12-20T16:13:59Z)
• Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review [0.0]
  Smart grid systems are critical to the power industry, however their sophisticated architectural design and operations expose them to a number of cybersecurity threats. Artificial Intelligence (AI)-based technologies are becoming increasingly popular for detecting cyber assaults in a variety of computer settings. The present AI systems are being exposed and vanquished because of the recent emergence of sophisticated adversarial systems such as Generative Adversarial Networks (GAN)
  arXiv  Detail & Related papers  (2022-02-14T21:41:36Z)
• Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training [71.7750435554693]
  We show that several state-of-the-art RL agents proposed for power system control are vulnerable to adversarial attacks. Specifically, we use an adversary Markov Decision Process to learn an attack policy, and demonstrate the potency of our attack. We propose to use adversarial training to increase the robustness of RL agent against attacks and avoid infeasible operational decisions.
  arXiv  Detail & Related papers  (2021-10-18T00:50:34Z)
• A Framework for Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems [41.470634460215564]
  We develop an extension to the MulVAL attack graph generation and analysis framework to incorporate cyberattacks on ML production systems. Using the proposed extension, security practitioners can apply attack graph analysis methods in environments that include ML components.
  arXiv  Detail & Related papers  (2021-07-05T05:58:11Z)
• Internet of Predictable Things (IoPT) Framework to Increase Cyber-Physical System Resiliency [0.0]
  This paper proposes the concept of the Internet of Predictable Things (IoPT) It incorporates advanced data analytics and machine learning methods to increase the resiliency of cyber-physical systems against cybersecurity risks.
  arXiv  Detail & Related papers  (2021-01-19T19:01:56Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

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.