A Novel Intrusion Detection Framework (IDF) using Machine Learning Methods

Abstract

An intrusion detection system is a critical security feature that analyses network traffic in order to avoid serious unauthorized access to network resources. For securing networks against potential breaches, effective intrusion detection is critical. In this paper, a novel Intrusion Detection Framework (IDF) is proposed. The three modules that comprise the suggested IDF are: (i) Data Pre-processing Module (DPM), (ii) Feature Selection Module (FSM), and Classification Module (CM). DPM collects and processes network traffic in order to prepare data for training and testing. The FSM seeks to identify the key elements for recognizing DPM intrusion attempts. An Improved Particle Swarm Optimization is used (IPSO). IPSO is a hybrid method that uses both filter and wrapper approaches to generate accurate and relevant information for the classification step that follows. Primary Selection Phase (PSP) and Completed Selection Phase (CSP) are the two consecutive feature selection phases in IPSO. PSP employs a filtering approaches to quickly identify the most significant features for detecting intrusion threats while eliminating those that are redundant or ineffective. In CSP, the next level of IPSO, this behavior reduces the computing cost. For accurate feature selection, CSP uses Binary Particle Swarm Optimization (Bi-PSO) as a wrapper approach. Based on the most effective features identified by FSM, The CM aims to identify intrusion attempts with the minimal processing time. Therefore, a K-Nearest Neighbor KNN classifier has been deployed. As a result, based on the significant features identified by the IPSO technique, KNN can accurately detect intrusion attacks with the least amount of processing time. The experimental results have shown that the proposed IDF outperforms other recent techniques using UNSW_NB-15 dataset. The accuracy, precision, recall, F1score, and processing time of the experimental outcomes of our findings were assessed. Our results were competitive with an accuracy of 99.8%, precision of 99.94%, recall of 99.85%, F1-score of 99.89%, and excursion time of 59.15s when compared to the findings of the current works.