ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection

Network Intrusion Detection (NID) systems are one of the most powerful forms of defense for protecting public and private networks. Most of the prominent methods applied to NID problems consist of Deep Learning methods that have achieved outstanding accuracy performance. However, even though they are effective, these systems are still too complex to interpret and explain. In recent years this lack of interpretability and explainability has begun to be a major drawback of deep neural models, even in NID applications. With the aim of filling this gap, we propose ROULETTE: a method based on a new neural model with attention for an accurate, explainable multi-class classification of network traffic data. In particular, attention is coupled with a multi-output Deep Learning strategy that helps to discriminate better between network intrusion categories. We report the results of extensive experimentation on two benchmark datasets, namely NSL-KDD and UNSW-NB15, which show the beneficial effects of the proposed attention mechanism and multi-output learning strategy on both the accuracy and explainability of the decisions made by the method.

Automated summary

Network Intrusion Detection (NID) systems are crucial for network protection, but existing deep learning methods are too complex to interpret. In this paper, a new neural model called ROULETTE is proposed, which combines attention mechanism and multi-output deep learning strategy for accurate and explainable classification of network traffic data. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method in terms of accuracy and explainability.