Towards Adversarial Resilience in Proactive Detection of Botnet Domain Names by using MTD

Dietz, Christian; Dreo, Gabi; Sperotto, Anna; Pras, Aiko

doi:10.1109/noms47738.2020.9110332

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…As an example, a universal firmware vulnerability observer (UFO) has been used to detect for verifying firmware security and discovering hidden backdoor in an IoT device [93]. M-ID3 and M-ID4 are methods that detect DER botnet devices (e.g., [94]) and DER worm devices communicating with the smart inverter, respectively. MR1 refers to a fast recovery strategy for a malware-infected smart inverter.…”

Section: B Malware Defense (Attacks #2-#6)mentioning

confidence: 99%

An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models

Ahn,

Kim,

Ahmad

et al. 2024

IEEE Trans. Power Electron.

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With high penetration of distributed energy resources (DERs), power systems are increasingly transforming into distributed power grids, which provide grid automation, decarbonization, and decentralization of critical assets. Smart inverters are key power-electronic devices that connect renewable energy and energy storage equipment to power grids. DER includes several intelligent grid functions, such as fault ride through, grid-voltage support, and reactive-power compensation, typically with real-time remote access, data exchange, and seamless over-the-air firmware updates in a cyber-physical environment. However, cybersecurity concerns arise due to extensive information exchange among DER and multiple stakeholders (e.g., utilities, aggregators, vendors, operators, and owners). Therefore, smart inverters account for a growing attack surface for the power grid. This article reviews the cybersecurity best practices and current recommendations for smart inverters and explores emerging cyber threats for smart inverters, including malware attacks and hardware attacks. Finally, we propose a new smart inverter security and resilience framework for developing cyber-resilient smart inverters against the advanced/future threat actors. This article establishes a resilience-by-design baseline reference for smart inverter cybersecurity teams, which bridges the gap between cybersecurity and power-electronics' communities.

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Section: B Malware Defense (Attacks #2-#6)mentioning

confidence: 99%

An Overview of Cyber-Resilient Smart Inverters Based on Practical Attack Models

Ahn,

Kim,

Ahmad

et al. 2024

IEEE Trans. Power Electron.

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Overview of Artificial Intelligence and Machine Learning

Zincir-Heywood¹,

Mellia²,

Diao³

2021

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

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