A Review of Intrusion Detection using Deep Learning

Authors

  • Levina Bisen M.Tech. Scholar, Department of CSE, Vaishnavi Institute of Technology and Science, Bhopal (M.P), India
  • Sumit Sharma Professor, Department of CSE, Vaishnavi Institute of Technology and Science , Bhopal (M.P), India

DOI:

https://doi.org/10.24113/ijosthe.v7i4.133

Keywords:

Anomaly, Intrusion Detection System, Supervised, Unsupervised, Web Security

Abstract

As network applications grow rapidly, network security mechanisms require more attention to improve speed and accuracy. The development of new types of intruders poses a serious threat to network security: although many tools for network security have been developed, the rapid growth of intrusion activity remains a serious problem. Intrusion Detection Systems (IDS) are used to detect intrusive network activity. Preventing and detecting unauthorized access to a computer is an IT security concern. Therefore, network security provides a measure of the level of prevention and detection that can be used to avoid suspicious users. Deep learning has been used extensively in recent years to improve network intruder detection. These techniques allow for automatic detection of network traffic anomalies. This paper presents literature review on intrusion detection techniques.

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References

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Published

2020-08-28

How to Cite

Bisen, L., & Sharma, S. . (2020). A Review of Intrusion Detection using Deep Learning. SMART MOVES JOURNAL IJOSTHE, 7(4), 15–20. https://doi.org/10.24113/ijosthe.v7i4.133

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