An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

Abstract

As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns regarding network security have increased. Accordingly, to deal with potential threats, research on Network Intrusion Detection Systems (NIDS) has been actively conducted. Among the various NIDS technologies, recently interest is focused on artificial intelligence(AI)-based anomaly detection systems, and various models have been proposed to improve the performance of NIDS. However, there still exists the problem of data imbalance, in which AI models cannot sufficiently learn malicious behavior and thus fail to detect network threats accurately. In this study, we propose a novel AI-based network intrusion detection system that can efficiently resolve the data imbalance problem and improve the performance of the previous systems. To address the aforementioned problem, we leveraged a state-of-the-art generative model that could generate plausible synthetic data for minor attack traffic. In particular, we focused on the reconstruction error and Wasserstein distancebased generative adversarial networks, and autoencoder-driven deep learning models. To demonstrate the effectiveness of our system, we performed comprehensive evaluations over various datasets and demonstrated that the proposed systems significantly outperformed the previous AI-based NIDS.

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

“…In order to solve this problem, various processing methods for imbalanced datasets have been proposed. Park et al [28] used generative adversarial network (GAN) to generate reasonable synthetic data for small sample data, which alleviates the data imbalance in an intrusion detection dataset. Finally, the superiority of the proposed method is proved on a variety of intrusion detection datasets.…”

An Innovative Intrusion Detection System for High-Density Communication Networks Using Artificial Intelligence

“…This makes the need for intrusion detection and protection of these high-density communications systems essential [11]. Intrusion detection is critical for high-density communications systems because the large number of devices and users makes it easier for malicious actors to gain access and take advantage of weak points in the system [12]. The challenge of intrusion detection in high-density communications systems can be addressed with the use of big data analytics and machine learning algorithms [13].…”

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

“…As a subfield of AI, machine learning (ML) involves data-driven algorithms that support the decision-making process of SOC analysts in detecting network intrusions (Anumol, 2015 ). In the current literature, several research works propose innovative AI-based intrusion detection methodologies (Das et al, 2019 ; Singh A. et al, 2022 ; Alkhudaydi et al, 2023 ; Maci et al, 2023 , 2024 ; Park et al, 2023 ; Coscia et al, 2024 ). A SIEM can integrate these techniques to enhance real-time analysis capabilities (Muhammad et al, 2023 ).…”