Intelligent Data Analytics Framework Using Ensemble-Attention Based Deep Learning Approach for Network Intrusion Detection

Abstract

The exponential growth of the internet and the proliferation of internetconnected
devices lead to the exhaustion of IPv4 addresses. To address this
challenge IPv6 was introduced. ICMPv6 plays an important role in IPv6 and is
prone to security vulnerabilities because it can be exploited for Distributed
Denial of Service (DDoS) Attacks. Attackers can flood the network with the
ICMPv6 messages to cause network disruptions. Henceforth, in our work, we
introduce an Ensemble-Attention Hybrid Deep Learning Approach for detecting
ICMPv6 messages flooding on IPv6 networks. An ensemble feature selection
technique is incorporated, that combines filter and wrapper methods to identify
essential features. To augment model precision, a transformer-based selfattention
mechanism is employed to ascertain attention weights assigned to the
selected features. By concatenating the ensemble feature selection with the selfattention
mechanism, a Convolutional Neural Network (CNN) model is
deployed to surpass the performance of existing methodologies. The
experimentation on a benchmark dataset is carried out and the evaluation is
based on metrics including False Positive Rate (FPR), detection accuracy, Fmeasure,
recall, and precision. The proposed approach was evaluated on a
benchmark dataset, achieving impressive performance metrics with a False
Positive Rate (FPR) of 0.16%, detection accuracy of 99.87%, an F-measure of
99.85%, recall of 99.84%, and precision of 99.86%, demonstrating its effectiveness
and reliability. Additionally, the findings indicate that the proposed approach
surpasses the performance of existing methodologies.