Investigation of Deep Learning Model Based Intrusion Detection in Traditional and Ad Hoc Networks

Evolution and proliferation of the state-of-the-art technologies enable a human life marked by convenience and ease of access. Due to this technological advancement, difficulties are also created which are observed across various segments. Security issues in the field of networking are no exceptions to this. Networking also experiences lots of troubles produced by diffident types of complex intrusions. As a countermeasure to these troubles, a sophisticated and well-organized system, defined as network intrusion detection system (NIDS), has been introduced. It is aimed not only at the improvement of detecting accuracy but also at the correct identification of unspecified attacks. Over the last few decades, several technologies are introduced for developing NIDS to ensure a high degree of privacy and reliability. With the progression of modern deep learning technologies, NIDS performs wonders in the field of information security. In this paper, a number of deep learning based NIDSs are reviewed in a wide range of network oriented infrastructures, viz., traditional and ad hoc networks. This paper mainly discusses about the applications, limitations and methodologies of various NIDSs to provide a complete and transparent overview of network security to the readers.