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The evolution in the attack scenarios has been such that finding efficient and optimal Network Intrusion Detection Systems (NIDS) with frequent updates has become a big challenge. NIDS implementation using machine learning (ML) techniques and updated intrusion datasets is one of the solutions for effective modeling of NIDS. This article presents a brief description of publicly available labeled intrusion datasets and ML techniques. Later a brief explanation of the literary works is given in which machine learning techniques are applied for NIDS implementation in different networking scenarios, such as traditional networks, cloud networks, Ad-Hoc, WSNs, and IoT networks. Hence, this article brings together publicly available intrusion datasets and machine learning techniques utilized in recent intrusion detection systems to reveal present-day challenges and future directions. This article also explains problems associated with NIDS. This will help researchers to enhance the existing NIDS models as well as to develop new effective models.

A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques