Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management

TL;DR

This paper introduces an Attention-GAN framework that combines attention mechanisms with generative adversarial networks to improve anomaly detection in cybersecurity, generating realistic attack data and focusing on relevant features for better threat identification.

Contribution

It presents a novel Attention-GAN model that enhances anomaly detection by integrating attention mechanisms with GANs, addressing data scarcity and evolving cyber threats.

Findings

Achieved 99.69% accuracy on KDD dataset

Attained 97.93% accuracy on CICIDS2017 dataset

Demonstrated high precision, recall, and F1-scores above 97%

Abstract

This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. In response to the challenges posed by the constantly evolving nature of cyber threats, the proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification. Integrating attention mechanisms with Generative Adversarial Networks (GANs) is a key feature of the proposed method. The attention mechanism enhances the model's ability to focus on relevant features, essential for detecting subtle and complex attack patterns. In addition, GANs address the issue of data scarcity by generating additional varied attack data, encompassing known and emerging threats. This dual approach ensures that the system remains relevant and effective against the continuously evolving cyberattacks. The KDD Cup and CICIDS2017 datasets were used to validate this model, which exhibited significant improvements in anomaly detection. It achieved an accuracy of 99.69% on the KDD dataset and 97.93% on the CICIDS2017 dataset, with precision, recall, and F1-scores above 97%, demonstrating its effectiveness in recognizing complex attack patterns. This study contributes significantly to cybersecurity by providing a scalable and adaptable solution for anomaly detection in the face of sophisticated and dynamic cyber threats. The exploration of GANs for data augmentation highlights a promising direction for future research, particularly in situations where data limitations restrict the development of cybersecurity systems. The attention-GAN framework has emerged as a pioneering approach, setting a new benchmark for advanced cyber-defense strategies.