A heuristic assisted cyber attack detection system using multi-scale and attention-based adaptive hybrid network

Abstract

Business domains have employed distributed platforms, and these domains use networks and communication services to send vital information that must be secured. To secure confidentiality, the information security system is introduced, which is described as the generation of the data, the network, and the hardware systems. Practically, all of our daily activities depend upon information and communication technology, which is vulnerable to threats. To rectify these issues, a deep learning-related cyber security system is developed to protect the data from various cyber-attacks. Initially, the cyber attacks are detected using Multi-scale and Attention-based Adaptive Hybrid Network (MA-AHNet), where the networks such as Dilated Long Short Term Memory (LSTM) and Deep Temporal Convolutional Network (DTCN) are integrated to construct MA-AHNet. The parameters from MA-AHNet are tuned with the support of the Fitness-based Ebola Optimization Algorithm (FEOA) to improve the detection performance. Then, the authorized user detection is carried out via the same MA-AHNet. Finally, the risk prediction is done via the same MA-AHNet to identify the level of risk in the network. These cyber-attacks, user authorization, and risk detection processes provide higher security. The experimental findings are validated with the traditional cyber security systems concerning various performance measures.