Implementation of African vulture optimization algorithm based on deep learning for cybersecurity intrusion detection

Abstract

The smart grid is an innovation that employs two-way communications to give innovative services to end consumers. Due to the severe contradictions in this connection, this system may be the target of numerous cyber-attacks. Intelligent grid networks can be protected by employing intrusion detection systems. IDS increases smart grid security by identifying malicious activity in the networks. However, current detection methods have several areas for improvement, including a high false alarm rate and low detection accuracy. The paper proposes an innovative intrusion detection strategy for intelligent grids combining DL-based and feature-based techniques. For this, the dataset is pre-processed, and pre-processing is done by utilizing min–max normalization. Then, features including mean, median, mode, standard deviation, information gain, mutual information, correlation coefficient, data percentiles, and autoregressive data are extracted. Next, African Vulture Optimization Algorithm organizes feature selection. Finally, DBN-LSTM is utilized for categorization to identify normal and attack packets. The developed method attains higher performance when compared with other existing techniques. Hence, the outcomes demonstrate that the AVOA-DBN-LSTM technique has a reliable potential for cybersecurity intrusion detection.