NETWORK INTRUSION DETECTION BASED ON CLASIFICATION

  • Anastasija Samardziska
  • Cveta Martinovska Bande University Goce Delcev Computer Science Faculty
Keywords: network intrusion detection, deep learning, metrics for classification evaluationation

Abstract

Network security is a serious concern for information technology users. Intrusion detection systems can detect malicious traffic and suspicious activity looking for signatures of known attacks. This paper describes a network intrusion detection system based on deep-learning approach. The system uses the ability of the convolutional neural network to detect attacks for which the system was not explicitly trained. The proposed solution can effectively identify network attacks with accuracy of 98% on the KDD99 dataset.

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Author Biography

Cveta Martinovska Bande, University Goce Delcev Computer Science Faculty
Published
2022-12-27
How to Cite
Samardziska, A., & Martinovska Bande, C. (2022). NETWORK INTRUSION DETECTION BASED ON CLASIFICATION. Balkan Journal of Applied Mathematics and Informatics, 5(2), 57-67. Retrieved from https://js.ugd.edu.mk/index.php/bjami/article/view/5256
Section
Articles