Towards a Comprehensive Framework for Cyber-Incident Response Decision Support in Smart Grids

• URL: http://arxiv.org/abs/2412.06254v2
• Date: Fri, 10 Jan 2025 12:40:22 GMT
• Title: Towards a Comprehensive Framework for Cyber-Incident Response Decision Support in Smart Grids
• Authors: Omer Sen, Yanico Aust, Martin Neumuller, Immanuel Hacker, Andreas Ulbig,
• Abstract summary: This paper presents a framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity. The proposed model aims to optimize the effectiveness and efficiency of grid cybersecurity efforts while offering insights into future grid management challenges.
• Score: 0.4077787659104315
• License:
• Abstract: The modernization of power grid infrastructures necessitates the incorporation of decision support systems to effectively mitigate cybersecurity threats. This paper presents a comprehensive framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity. By analyzing risk attributes and optimizing defense strategies, this framework enables grid operators to prioritize critical security measures. Additionally, this paper incorporates findings on decision-making processes in intelligent power systems to present a comprehensive approach to grid cybersecurity. The proposed model aims to optimize the effectiveness and efficiency of grid cybersecurity efforts while offering insights into future grid management challenges.

Related papers

• Integrating Cybersecurity Frameworks into IT Security: A Comprehensive Analysis of Threat Mitigation Strategies and Adaptive Technologies [0.0]
  The cybersecurity threat landscape is constantly actively making it imperative to develop sound frameworks to protect the IT structures. This paper aims to discuss the application of cybersecurity frameworks into the IT security with focus placed on the role of such frameworks in addressing the changing nature of cybersecurity threats. The discussion also singles out such technologies as Artificial Intelligence (AI) and Machine Learning (ML) as the core for real-time threat detection and response mechanisms.
  arXiv  Detail & Related papers  (2025-02-02T03:38:48Z)
• A Complexity-Informed Approach to Optimise Cyber Defences [0.0]
  This paper introduces a novel complexity-informed approach to cybersecurity management, addressing the challenges found within complex cyber defences. We adapt and extend the complexity theory to cybersecurity and develop a quantitative framework that empowers decision-makers with strategies to de-complexify defences, identify improvement opportunities, and resolve bottlenecks.
  arXiv  Detail & Related papers  (2025-01-26T16:04:13Z)
• Secure Resource Allocation via Constrained Deep Reinforcement Learning [49.15061461220109]
  We present SARMTO, a framework that balances resource allocation, task offloading, security, and performance. SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments.
  arXiv  Detail & Related papers  (2025-01-20T15:52:43Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach [58.93030774141753]
  Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence. This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning. We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
  arXiv  Detail & Related papers  (2024-11-17T23:06:20Z)
• Enhancing cybersecurity defenses: a multicriteria decision-making approach to MITRE ATT&CK mitigation strategy [0.0]
  This paper proposes a defense strategy for the presented security threats by determining and prioritizing which security control to put in place. This approach helps organizations achieve a more robust and resilient cybersecurity posture.
  arXiv  Detail & Related papers  (2024-07-27T09:47:26Z)
• 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)
• Fortify Your Defenses: Strategic Budget Allocation to Enhance Power Grid Cybersecurity [1.672787996847537]
  Given potential cyber-attack sequences for a cyber-physical component in the power grid, find the optimal manner to allocate an available budget to implement necessary preventive mitigation measures. We formulate the problem as a mixed integer linear program to identify the optimal budget partition and set of mitigation measures. We show how altering the budget allocation for tasks such as asset management, cybersecurity infrastructure improvement, incident response planning and employee training affects the choice of the optimal set of preventive mitigation measures.
  arXiv  Detail & Related papers  (2023-12-20T23:01:35Z)
• Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey [15.633226785669203]
  This survey aims to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. An integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers.
  arXiv  Detail & Related papers  (2023-05-09T10:59:56Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

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.