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Implementation of deep packet inspection in smart grids and industrial Internet of Things: Challenges and opportunities

机译:在智能电网和工业物联网中实施深度包检查:挑战与机遇

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摘要

Upgrading a power grid to a smart grid is a challenging task. For example, since power grids were originally developed to support unidirectional communications, the migration process requires architectural and cybersecurity upgrades due to the integration of devices using bidirectional communication. The integration of these devices opens numerous avenues for cyber attacks, although they also enable numerous capabilities in smart grids. To protect the smart grid from cyber threats, it is important for industry and academia to explore and implement practical cybersecurity models together, for example collaboratively designing and developing suitable smart grid testbeds to facilitate research. In this paper, we survey existing literature relating to the infrastructure and communications for the energy sector and smart grids. Specifically, we study existing recommendations and models from government agencies (e.g. NIST and DOE) and academia, and evaluate deep packet inspection (DPI) approaches as a security tool for smart grids. We also propose a conceptual SDN-based security monitoring framework based on SDN, Network Behavior Analysis (NBA), Deep Learning Models, and DPI attack corroboration, as well as a conceptual forensic-driven security monitoring framework where digital forensics and investigation capabilities are integrated to inform security monitoring.
机译:将电网升级到智能电网是一项艰巨的任务。例如,由于电网最初是为支持单向通信而开发的,由于使用双向通信的设备集成,迁移过程需要架构和网络安全升级。这些设备的集成为网络攻击开辟了许多渠道,尽管它们还启用了智能电网中的众多功能。为了保护智能电网免受网络威胁,业界和学术界共同探索和实施实用的网络安全模型非常重要,例如,共同设计和开发合适的智能电网测试平台以促进研究。在本文中,我们调查了有关能源部门和智能电网的基础设施和通信的现有文献。具体来说,我们研究了来自政府机构(例如NIST和DOE)和学术界的现有建议和模型,并评估了深包检查(DPI)方法作为智能电网的安全工具。我们还提出了基于SDN,网络行为分析(NBA),深度学习模型和DPI攻击确证的基于SDN的概念性安全监视框架,以及集成了数字取证和调查功能的概念性取证驱动的安全监视框架。通知安全监控。

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