A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning

Abstract

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed.

Robust genetic machine learning ensemble model for intrusion detection in network traffic

“…CFS is frequently paired with search algorithms like forward selection, backward elimination, and bi-directional search to take into consideration the significant computational burden of evaluating all candidate feature subsets. In this investigation, we used the sci-kit-learn version of CFS 45 , which employs symmetrical uncertainty 46 as the correlation metric and stops searching the subset space after five successive fully enlarged non-improving subsets.…”

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

“…In the context of H-IoT, machine learning is beneficial for remote monitoring and real-time treatment of diseases [19], [148]. ML algorithms such as Support Vector Machines (SVMs), decision trees, random forests, and Artificial Neural Networks (ANNs) can analyze huge volumes of medical data collected by healthcare-related smart devices, including vital signs and medical histories [149]. In this process, ML techniques are applied to analyze massive datasets to find patterns and generate insights that may assist clinical decisions, improve patient outcomes, and reduce healthcare expenditures.…”

Subject review: Intrusion Detection System (IDS) and Intrusion Prevention System (IPS)

“…NIDS come in two general forms; signature based NIDS and heuristic based NIDS. These two types of NIDS provide a varying degree [5].…”

“…Hybrid detection combines both of the aforementioned detections. Generally, they have a lower false detection rate than anomaly techniques and can discover new attacks [5].…”