MLTs-ADCNs: Machine Learning Techniques for Anomaly Detection in Communication Networks

Abstract

From a security perspective, the research of the jeopardized 6G wireless communications and its expected ultra-densified ubiquitous wireless networks urge the development of a robust intrusion detection system (IDS) with powerful capabilities which could not be sufficiently provided by the existing conventional systems. IDSs are still insufficient against continuous renewable unknown attacks on the wireless communication networks, especially with the new highly vulnerable networks, leading to low accuracy and detection rate with high (false-negative and false-positive) rates. To this end, this paper proposed a novel anomaly detection in communication networks by using an ensemble learning (EL) algorithm-based anomaly detection in communication networks (ADCNs). EL-ADCNs consists of four main stages; the first stage is the preprocessing steps. The feature selection method is the second stage. It adopts the proposed hybrid method using correlation with the random forest algorithm of ensemble learning (CFS–RF). It reduces dimensionality and retrieves the best subset feature of all the three datasets (NSL_KDD, UNSW_NB2015, and CIC_IDS2017) separately. The third stage is using hybrid EL algorithms to detect intrusions. It involves modifying two classifiers (i.e., random forest (RF), and support vector machine (SVM)) to apply them as adaboosting and bagging EL Algorithms; using the voting average technique as an aggregation process. The final stage is testing the proposal using binary and multi-class classification forms. The experimental results of applying 30, 35, and 40 features of the proposed system to the three datasets achieved the best results of a 99.6% accuracy with a 0.004 false-alarm rate for NSL_KDD, a 99.1% accuracy with a 0.008 false-alarm rate for UNSW_NB2015, and a 99.4% accuracy with a 0.0012 false-alarm rate for CIC_IDS2017.

Keywords

• Adaboosting algorithm
• Bagging algorithm
• correlation feature selection
• ensemble method
• intrusion detection systems