The quality or state of being secure is the crucial concern of our daily life usage of any network. However, with the rapid breakthrough in network technology, attacks are becoming more trailblazing than defenses. It is a daunting task to design an effective and reliable intrusion detection system (IDS), while maintaining minimal complexity. The concept of machine learning is considered an important method used in intrusion detection systems to detect irregular network traffic activities. The use of machine learning is the current trend in developing IDS in order to mitigate false positives (FP) and False Negatives (FN) in the anomalous IDS. This paper targets to present a holistic approach to intrusion detection system and the popular machine learning techniques applied on IDS systems, bearing In mind the need to help research scholars in this continuous burgeoning field of Intrusion detection (ID).

Network usage has become a paramount aspect of life, therefore, securing our networks is crucial. The world is experiencing a rapid breakthrough of internet usage, most especially with the concept of internet of things (IoT), now internet of everything (IoE. ). Real network data is rowdy, noisy and inconsistent. These issues with the data influences the performance of intrusion detection systems (IDS) and develop manifold of false alarms. Feature selection technique is used to remove the inconsistent and rowdy data from a large data set and presents a refined set of data. This research work adopts the use of two distinct feature selection technique in parallel: ReliefF ranking and particle swarm optimization, using linear discriminant analysis (LDA) and logistic regression (LR) as the machine learners, to first clean the data, train the classifiers, and subsequently classify new instances. The results showed that, the combination of the ReliefF with the ensemble machine learning (Linear Discriminant Analysis and Logistic Regression) has a higher classification accuracy of 99.7% compared to the Particle swarm optimization (PSO) which attained an accuracy of 98.6%.