مقاله انگلیسی تشخیص حمله سایبری تزریق داده های نادرست
Highlights
Abstract
Keywords
1. Introduction
2. Cyber–physical DC microgrids
3. Attack detection design
4. Performance validation
5. Conclusion
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgments
Appendix.
References
Abstract
DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.
1. Introduction
DC microgrids (MGs), known as next-generation power systems, have received significant attention in recent years because of their ability to transmit power from renewable energy sources and energy storage devices to various loads with greater efficiency and reliability than the AC grid. As a distributed power supply, the DC MG can be operated independently or connected to the utility grid. Such applications can be found in power generations [1], smart houses [2], transportation systems [3], etc.
Due to the rapid developments of the Industry 4.0 paradigm, information technology-based solutions have been widely used in industrial processes. The revolutionary changes have seen the emergence of cyber–physical systems where large amounts of data are exchanged between multiple devices in real time [4]. Accordingly, the framework of DC MG tends to be more distributed, intelligent and tightly integrated with the network. However, due to the strong reliance on the communication technologies, DC MGs are more vulnerable to security threats [5] and have a higher risk of being compromised by malicious attackers.
In general, the functionality of a potential microgrid controller relies heavily on the reliability of the data received by the measurement devices or sensors. For example, if the sensors or communication links are compromised by an attacker, the controllers may receive faulty data and therefore make inappropriate control decisions [6], leading to the undesirable power-sharing [7], frequency oscillating [8] and stability issues [9]. As a result, renewable energy generating units may not be able to produce the maximum amount of available power from nature or meet the appropriate power sharing between microgrids, and energy storage devices may not be able to provide the required amount of power or operate with optimal economic dispatch [10]. More seriously, attackers may be able to disrupt the system without adequate security protection in terms of hardware or software policies, leading to significant social losses. Examples include the nuclear facility struck by Stuxnet malware [11], power outage event [12], [13] and nuclear plant blackout accident [14]. Considering the huge impact of attacks on microgrid systems, it is vital to provide an effective detection scheme to counter cyber attacks.