Unleashing the Power of Machine and Deep Learning for Advanced Network Intrusion Detection: An Analysis and Exploration

The rapidly growing advance and complex internet and communication area result into a big network and data. With the increasing wide network coverage, it gives invitation to many novel attacks to be generated and hard challenge to telecommunication industry to secure the huge data and timely and accurately detect these novel attacks. Intrusion detection system is an effective way to prevent from intruders to inspection, to steal confidential information, to money and assets crisis and integrity. Even though thousands of research IDS still faces challenges to deal with modern attacks and detecting them within time. In recent time, IDS centered on ML and DL are deployed for possible solution to detect network threats in an effective manner. In this paper, clarifies the fundaments of IDS and ML and DL techniques implemented for designing network and host based IDSs. This article has comprehensive reviewed of recent 30 ML-DL intrusion detection model in domain of AIDS and NIDS model from the year 1998 to 2022 on basis of their performance, dataset applied and evaluation measures includes detection rate, accuracy, precision, recall and true positive rate. Pointing shortcoming of proposed methods and outlines potential areas for future research for enlightening advance understanding of cyber threat detection.