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arXiv 2412.06255cs.CR
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智能电网中多阶段攻击与防御机制的仿真

Simulation of Multi-Stage Attack and Defense Mechanisms in Smart Grids

Omer Sen, Bozhidar Ivanov, Christian Kloos, Christoph Zol_, Philipp Lutat, Martin Henze, Andreas Ulbig

AI总结:

针对智能电网安全数据不足问题,本文构建模块化仿真环境,利用攻击树和博弈论建模多阶段攻防并生成真实数据,以训练和评估入侵检测与决策支持模型。

摘要:

电网是对公共安全和福利至关重要的关键基础设施。随着其对数字技术的依赖不断增加,它面对复杂网络威胁时的脆弱性也在增加,这些威胁可能严重扰乱运行。入侵检测和决策支持系统等有效保护措施对于缓解这些风险至关重要。机器学习在该领域具有巨大潜力,但其有效性受到保密性和访问限制导致的高质量数据有限可用性的制约。为解决这一问题,我们引入了一个仿真环境,用于复现电网的基础设施和通信动态。该环境能够对复杂的多阶段网络攻击和防御响应进行建模,使用攻击树勾勒攻击者策略,并采用博弈论方法对防御者行动进行建模。该框架生成多样、真实的攻击数据,以训练用于检测和缓解网络威胁的机器学习算法。它还提供了一个受控、灵活的平台,用于评估包括先进决策支持系统在内的新兴安全技术。该环境具有模块化和可扩展性,便于在不依赖外部组件的情况下集成新场景。它在一条连贯链路中支持场景生成、数据建模、映射、潮流仿真和通信流量分析,在一致条件下捕获网络安全调查的所有相关数据。对通信协议和电网运行的详细建模提供了关于攻击传播的洞察,同时数据集在实验室环境中接受验证,以确保现实世界适用性。这些数据集被用于训练入侵检测机器学习模型,重点关注其在电网运行中识别复杂攻击模式的能力。

英文摘要:

The power grid is a critical infrastructure essential for public safety and welfare. As its reliance on digital technologies grows, so do its vulnerabilities to sophisticated cyber threats, which could severely disrupt operations. Effective protective measures, such as intrusion detection and decision support systems, are essential to mitigate these risks. Machine learning offers significant potential in this field, yet its effectiveness is constrained by the limited availability of high-quality data due to confidentiality and access restrictions. To address this, we introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics. This environment enables the modeling of complex, multi-stage cyber attacks and defensive responses, using attack trees to outline attacker strategies and game-theoretic approaches to model defender actions. The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats. It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems. The environment is modular and scalable, facilitating the integration of new scenarios without dependence on external components. It supports scenario generation, data modeling, mapping, power flow simulation, and communication traffic analysis in a cohesive chain, capturing all relevant data for cyber security investigations under consistent conditions. Detailed modeling of communication protocols and grid operations offers insights into attack propagation, while datasets undergo validation in laboratory settings to ensure real-world applicability. These datasets are leveraged to train machine learning models for intrusion detection, focusing on their ability to identify complex attack patterns within power grid operations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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