HPAC-IDS: A Hierarchical Packet Attention Convolution for Intrusion Detection System
Anass Grini, Btissam El Khamlichi, Abdellatif El Afia, Amal El, Fallah-Seghrouchni

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…
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