HPAC-IDS: A Hierarchical Packet Attention Convolution for Intrusion Detection System

TL;DR

This paper presents HPAC-IDS, a hierarchical packet attention convolutional system for intrusion detection that achieves high accuracy and robustness against adversarial attacks on network traffic data.

Contribution

The paper introduces a novel hierarchical attention and convolution-based model for intrusion detection, enhancing robustness against adversarial attacks.

Findings

High detection accuracy on CIC-IDS2017 dataset

Robustness against FGSM, PGD, and WGAN attacks

Outperforms state-of-the-art models in detection and adversarial resilience

Abstract

This research introduces a robust detection system against malicious network traffic, leveraging hierarchical structures and self-attention mechanisms. The proposed system includes a Packet Segmenter that divides a given raw network packet into fixed-size segments that are fed to the HPAC-IDS. The experiments performed on CIC-IDS2017 dataset show that the system exhibits high accuracy and low false positive rates while demonstrating resilience against diverse adversarial methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Wasserstein GAN (WGAN). The model's ability to withstand adversarial perturbations is attributed to the fusion of hierarchical attention mechanisms and convolutional neural networks, resulting in a 0% to 10% adversarial attack severity under tested adversarial attacks with different segment sizes, surpassing the state-of-the-art model in detection performance and adversarial attack robustness.