In recent years, the secure operation of power systems has grasped the focus of researchers' attention, and fuzzing test-based vulnerability mining has become a commonly used tool for evaluating its stability and robustness in the face of various anomalies. In fuzzing testing, test case generation is concerned with the ultimate performance of vulnerability mining, however, existing techniques generally focus only on coverage and not on the vulnerable parts. Therefore, based LSTM arrays that combined with residual connections, we propose a novel approach to learn the vulnerable parts of the power system to guide the process of fuzzing by utilizing the powerful feature representation capability of neural networks. Experiments show that the improved method can achieve faster convergence and higher vulnerability detection rate of neural networks, while the FNR of which is 20.4% and TPR is 79.6%.
With the advancement of digital transformation, digital relay protection devices have replaced traditional protection devices and become an important part of the stable operation of the power system. The IEC 61850 standard is the basic protocol for real-time communication and data exchange between key substation equipment and is widely used in substation automation equipment and protection devices. However, IEC 61850 has a man-in-the-middle attack security risk. This paper studies the network attack of IEC 61850 Generic Object-Oriented Substation Event (GOOSE) protocol. By exploiting protocol loopholes to achieve penetration attacks, forged GOOSE data frames are injected into the interval layer of the substation communication network, and sent to multiple relay protection devices at the same time to carry out coordinated network attacks, which can cause multiple relay protection devices to incorrectly issue trip commands , resulting in cascading failures in the grid, which eventually lead to power outages. Finally, simulation experiments are carried out through hardware loop simulation and real-time digital simulator (RTDS) to verify the effectiveness of the attack simulation.
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