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Register a new userA Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid
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Added by
Haftu Reda
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system that benefits from the revolutions happening in the communications and the fast adoption of Internet of Things devices. While the synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality and availability. False data injection (FDI) appears to be among the most critical cyberattacks and has been a focal point interest for both research and industry. To this end, this paper presents a comprehensive review in the recent advances of the defence countermeasures of the FDI attacks in the Smart Grid infrastructure. Relevant existing literature are evaluated and compared in terms of their theoretical and practical significance to the Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack detection researches are identified, and a number of future research directions are recommended.
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The cybersecurity of smart grids has become one of key problems in developing reliable modern power and energy systems. This paper introduces a non-stationary adversarial cost with a variation constraint for smart grids and enables us to investigate the problem of optimal smart grid protection against cyber attacks in a relatively practical scenario. In particular, a Bayesian multi-node bandit (MNB) model with adversarial costs is constructed and a new regret function is defined for this model. An algorithm called Thompson-Hedge algorithm is presented to solve the problem and the superior performance of the proposed algorithm is proven in terms of the convergence rate of the regret function. The applicability of the algorithm to real smart grid scenarios is verified and the performance of the algorithm is also demonstrated by numerical examples.
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The congestion control algorithm of TCP relies on correct feedback from the receiver to determine the rate at which packets should be sent into the network. Hence, correct receiver feedback (in the form of TCP acknowledgements) is essential to the goal of sharing the scarce bandwidth resources fairly and avoiding congestion collapse in the Internet. However, the assumption that a TCP receiver can always be trusted (to generate feedback correctly) no longer holds as there are plenty of incentives for a receiver to deviate from the protocol. In fact, it has been shown that a misbehaving receiver (whose aim is to bring about congestion collapse) can easily generate acknowledgements to conceal packet loss, so as to drive a number of honest, innocent senders arbitrarily fast to create a significant number of non-responsive packet flows, leading to denial of service to other Internet users. We give the first formal treatment to this problem. We also give an efficient, provably secure mechanism to force a receiver to generate feedback correctly; any incorrect acknowledgement will be detected at the sender and cheating TCP receivers would be identified. The idea is as follows: for each packet sent, the sender generates a tag using a secret key (known to himself only); the receiver could generate a proof using the packet and the tag alone, and send it to the sender; the sender can then verify the proof using the secret key; an incorrect proof would indicate a cheating receiver. The scheme is very efficient in the sense that the TCP sender does not need to store the packet or the tag, and the proofs for multiple packets can be aggregated at the receiver. The scheme is based on an aggregate authenticator. In addition, the proposed solution can be applied to network-layer rate-limiting architectures requiring correct feedback.
Electric power grids are at risk of being compromised by high-impact cyber-security threats such as coordinated, timed attacks. Navigating this new threat landscape requires a deep understanding of the potential risks and complex attack processes in energy information systems, which in turn demands an unmanageable manual effort to timely process a large amount of cross-domain information. To provide an adequate basis to contextually assess and understand the situation of smart grids in case of coordinated cyber-attacks, we need a systematic and coherent approach to identify cyber incidents. In this paper, we present an approach that collects and correlates cross-domain cyber threat information to detect multi-stage cyber-attacks in energy information systems. We investigate the applicability and performance of the presented correlation approach and discuss the results to highlight challenges in domain-specific detection mechanisms.
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