Securing Multi-Environment Networks using Versatile Synthetic Data Augmentation Technique and Machine Learning Algorithms

The emergence of new network architectures, protocols, and tools has made it easier for cybercriminals to launch attacks using AI-based tools, presenting challenges in network security. To protect such systems, a versatile malicious traffic detection system is required that can identify attacks regardless of the type of traffic coming toward the network. In this paper, a system is proposed that can singly analyze multi-environment traffic (IoT and traditional IP-based) to detect malicious activity. The existing techniques for managing Multi-Environment traffic are inefficient due to the absence of AI utilization. To overcome these issues, the proposed approach generates a novel multienvironment traffic dataset by merging existing network datasets containing both traditional IP-based traffic and IoT network traffic. Synthetic Data Augmentation TEchnique (S-DATE) is also proposed to overcome the problem of imbalanced data distribution in the new multi-environment dataset. The results show that the utilization of S-DATE results in faster machine learning model convergence and an improvement in the detection rate of normal and abnormal traffic. The proposed approach achieves an impressive overall detection rate of 0.991 and is statistically significant compared to other state-of-the-art approaches.