NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability

TL;DR

This paper introduces neural network models for network intrusion detection that use a time window approach with trainable activations, achieving high accuracy and analyzing their generalization across different datasets.

Contribution

It proposes a novel NIDS neural network architecture with trainable activation functions inspired by Kolmogorov-Arnold networks, focusing on simplicity and high accuracy.

Findings

Achieves over 99% training accuracy with 20 features.

Shows significant performance decline when generalizing across datasets.

Fewer parameters and well-tuned activations improve stability and accuracy.

Abstract

This paper presents neural networks for network intrusion detection systems (NIDS), that operate on flow data preprocessed with a time window. It requires only eleven features which do not rely on deep packet inspection and can be found in most NIDS datasets and easily obtained from conventional flow collectors. The time window aggregates information with respect to hosts facilitating the identification of flow signatures that are missed by other aggregation methods. Several network architectures are studied and the use of Kolmogorov-Arnold Network (KAN)-inspired trainable activation functions that help to achieve higher accuracy with simpler network structure is proposed. The reported training accuracy exceeds 99% for the proposed method with as little as twenty neural network input features. This work also studies the generalization capability of NIDS, a crucial aspect that has not been adequately addressed in the previous studies. The generalization experiments are conducted using CICIDS2017 dataset and a custom dataset collected as part of this study. It is shown that the performance metrics decline significantly when changing datasets, and the reduction in performance metrics can be attributed to the difference in signatures of the same type flows in different datasets, which in turn can be attributed to the differences between the underlying networks. It is shown that the generalization accuracy of some neural networks can be very unstable and sensitive to random initialization parameters, and neural networks with fewer parameters and well-tuned activations are more stable and achieve higher accuracy.