A Review of Existence Intrusions Detection-Based Machine Learning Datasets of Future Generation Networks

"Innovative technologies of future generation networks such as Cyber-Physical System (CPS), Mobile Ad Hoc Network (MANET), Vehicular Ad-Hoc Network (VANET), Internet of Things (IOT), and Wireless network commonly known as Wi-Fi have emerged, which require a distinguished understanding of the main challenges and constraints that face the design and implementation of an Intrusion Detection Systems (IDS) for such type of networks. Moreover, a dramatic increase in the rate of cyber-attacks has increased, and new cases of intrusions, bugs, novel attacking tactics, and vulnerabilities are evolving daily. Intrusion Detection Systems (IDS) are one of the solutions against these attacks. Thus, IDS needs to improve its performance in terms of its ability to detect new attacks and respond to threats. Getting suitable datasets for evaluating various research designs in IDS design domains is a significant challenge"“. The machine learning (ML) design approach can quickly identify trends and patterns of intrusions, bugs, tactics, and cyber vulnerabilities with minimum human intervention. This paper reviews datasets for the research community. Furthermore, it explores the challenges of Dataset for intrusion detection based on Machine learning. It glances through a period of 6 years of intrusion detection datasets, explores what is currently applicable, outlines criteria for selecting the best Dataset, and explores future directions for creating relevant datasets.