An Efficiency Optimization for Network Intrusion Detection System

Abstract

With the enormous rise in the usage of computer networks, the necessity for safeguarding these networks is also increased. Network intrusion detection systems (NIDS) are designed to monitor and inspect the activities in a network. NIDS mainly depends on the features of the input network data as these features give information on the behaviour nature of the network traffic. The irrelevant and redundant network features negatively affect the efficacy and quality of NIDS, particularly its classification accuracy, detection time and processing complexity. In this paper, several feature selection techniques are applied to optimize the efficiency of NIDS. The categories of the applied feature selection techniques are the filter, wrapper and hybrid. Support vector machine (SVM) is employed as the detection model to classify the network connections behaviour into normal and abnormal traffic. NIDS is trained and tested on the benchmark NSL-KDD dataset. The performance of the applied feature selection techniques is compared with each other and the results are discussed. Evaluation results demonstrated the superiority of the wrapper techniques in providing the highest classification accuracy with the lowest detection time and false alarms of the NIDS.

Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method

“…Having considered other studies in this respect, when Gu, et al [41] investigated incorporating the Self-Correlation Coefficient as the procedure of the filter method on the CICIDS2017 dataset, they could achieve the false-positive rate of 29.30. In another study, Sakr, et al [56] investigated the false-positive rates in detecting intrusions using the filter and wrapper methods. Principal Component Analysis (PCA), Correlation Feature Selection (CFS), and information gain (IG) were the procedures of the filter method; Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) were the procedures of the wrapper method in the study by Sakr, et al [56].…”

“…In another study, Sakr, et al [56] investigated the false-positive rates in detecting intrusions using the filter and wrapper methods. Principal Component Analysis (PCA), Correlation Feature Selection (CFS), and information gain (IG) were the procedures of the filter method; Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) were the procedures of the wrapper method in the study by Sakr, et al [56]. The results of the study with respect to the obtained false-positive rates are shown in the Table 5 below.…”

“…The results of the study with respect to the obtained false-positive rates are shown in the Table 5 below. The false-positive rates reflect the application of filter and filter-wrapper feature-selection methods, including when Sakr, et al [56] did not apply any feature-selection method.…”

Security Professionals Must Reinforce Detect Attacks to Avoid Unauthorized Data Exposure

“…The best scenario is for organizations to focus on security implementations and employee management [17]. The analysis of the human factors when focusing on internal and external attack origin, along with the ability to efficiently use anomaly detection, is vital [56]. Further understanding of human behavior research can benefit cybersecurity to provide more insights regarding the evolving challenges regarding cyber threats [20].…”