Title: Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach

Authors: G. Gowthami; S. Silvia Priscila

Addresses: Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India ' Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

Abstract: Network intrusion detection system (NIDS) is important for securing network information. Neural network (NN) has recently been used for NIDS, which gained prominence results. Conventional neural network (CNN) has been introduced in network traffic data because of its single structure. The classification of assaults will no longer be useful due to redundant or inefficient features. Tuna swarm optimisation (TSO) has been introduced for feature selection (FS). First, pre-processing and feature extraction stages enable more efficient processing of features if handled independently. In order to examine the exploration space accuracy and position the best features, the second feature selection step of the TSO methodology involved selecting a subset of features by reducing the number of features. Lastly, multimodal deep auto encoder (MDAE) and gated recurrent unit (GRU) allow deep multimodal-sequential-hierarchical progressive network (DMS-HPN) intrusion detection method. Its DMS-HPN technique would routinely learn the temporal features among neighbouring network connections, simultaneously integrating diverse feature information inside a network. Datasets like UNSW-NB15 and CICIDS 2017 assess the effectiveness of the proposed DMS-HPN approach. Classification algorithms are evaluated via precision, recall, F-measure, and accuracy. Compared to conventional classifiers, the presented DMS-HPN classifier achieves the greatest accuracy.

Keywords: network intrusion detection systems; NIDS; feature selection; FS; multimodal deep auto encoder; MDAE; conventional neural network; CNN; gated recurrent unit; GRU; tuna swarm optimisation; TSO.

DOI: 10.1504/IJCCBS.2023.136338

International Journal of Critical Computer-Based Systems, 2023 Vol.10 No.4, pp.355 - 374

Received: 16 Jun 2023
Accepted: 06 Nov 2023
Published online: 30 Jan 2024 *