Machine learning techniques for accurate classification and detection of intrusions in computer network

Abstract

An incursion into the computer network or system in issue occurs whenever there is an attempt made to circumvent the defences that are in place. Training and examination are the two basic components that make up the intrusion detection system (IDS) and each one may be analysed separately. During training, a number of distinct models are built, each of which is able to distinguish between normal and abnormal behaviours that are included within the dataset. This article proposes a combination of ant colony optimization (ACO) and the firefly approach for feature selection. The final outcome of giving careful thought to the selection of features will eventually result in greater accuracy of categorisation. When classifying various sorts of features, we make use of a wide variety of machine learning (ML) algorithms, including AdaBoost, gradient boost, and Bayesian network (BN), amongst others. The tests and assessments made use of data obtained from three distinct datasets, namely NSL-KDD, UNSW-NB15, and CICIDS 2017. The degree of performance of an individual may be broken down into its component parts, which include the F1 score, accuracy, precision, and recall. Gradient boost performs far better when it comes to recognising and classifying incursions.

Enhancing intrusion detection system using rectified linear unit function in pigeon inspired optimization algorithm

“…After training, an IDS can use the knowledge it has acquired to predict unauthorized activity more accurately than an IDS that does not use machine learning, using machine learning in IDS can also help reduce the number of false positives that occur, which are cases where an IDS flags legitimate activity as invalid. By leveraging machine learning capabilities to learn more complex patterns [6].…”

Machine learning approach for intrusion detection system using dimensionality reduction

“…Their system outperformed the other available intrusion attack techniques. Paricherla et al [19] presented a two-dimensionality reduction approach (linear discriminant analysis), to address concerns with extra dimensions in datasets. Ali and Jawhar [20], in their paper titled "Detecting network attacks model based on a convolution neural network", have discussed convolutional neural network (CNN) networks using deep learning convolutional networks.…”