A Review of Intrusion Detection Datasets and Techniques

  • Sadhana Patidar M. Tech. Scholar Department of CSE Radharaman Institute of Technology & Science, Bhopal, India
  • Priyanka Parihar Assistant Professor Department of CSE Radharaman Institute of Technology & Science, Bhopal, India
  • Chetan Agrawal Assistant Professor Department of CSE Radharaman Institute of Technology & Science, Bhopal, India

Abstract

As network applications grow rapidly, network security mechanisms require more attention to improve speed and accuracy. The evolving nature of new types of intrusion poses a serious threat to network security: although many network securities tools have been developed, the rapid growth of intrusive activities is still a serious problem. Intrusion detection systems (IDS) are used to detect intrusive network activity. In order to prevent and detect the unauthorized access of any computer is a concern of Computer security. Hence computer security provides a measure of the level associated with Prevention and Detection which facilitate to avoid suspicious users. Deep learning have been widely used in recent years to improve intrusion detection in networks. These techniques allow the automatic detection of network traffic anomalies. This paper presents literature review on intrusion detection techniques.

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Published
March 2020
How to Cite
Patidar, S., Parihar, P., & Agrawal, C. (2020). A Review of Intrusion Detection Datasets and Techniques. SMART MOVES JOURNAL IJOSCIENCE, 6(3), 9. https://doi.org/10.24113/ijoscience.v6i3.277