The Capability of Wavelet Convolutional Neural Network for Detecting Cyber Attack of Distributed Denial of Service in Smart Grid

• Published in: 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) pp. 413 - 418
• Main Authors: Monday, Happy Nkanta, Li, Jian Ping, Nneji, Grace Ugochi, Yutra, Abel Zenebe, Lemessa, Bona Debela, Nahar, Saifun, James, Edidiong Christopher, Haq, Amin Ul
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.12.2021
• Subjects: Continuous wavelet transform; Continuous wavelet transforms; Convolutional neural network; Cyber security; DDoS attack; Denial-of-service attack; Distributed denial of service attack; Feature extraction; Information processing; Media; Smart grid; Time-frequency analysis; Wavelet domain
• ISSN: 2576-8964
• DOI: 10.1109/ICCWAMTIP53232.2021.9674080

The electrical system's dependability, security, and efficiency are all improved through smart grid technologies. Its dependence on digital communication technology, on the other hand, introduces new risks and vulnerabilities that should be examined for the purpose to providing effective and trustworthy service delivery. This study presents a method for the detection of distributed denial of service (DDoS) attacks on smart grid infrastructure. Continuous wavelet transform (CWT) is used in the suggested approach to convert one-dimensional traffic data to two-dimensional time-frequency domain scalogram as the input to the wavelet convolutional neural network (WavCovNet) to detect anomalous behavior in the data by distinguishing attack features from normal patterns. Our results demonstrate that the proposed approach detects DDoS attacks with a high rate of detection and with a very low rate of false alarm.