Automatic Intrusion Detection Using Optimal Features with Adaptive Bi-Directional Long Short Term Memory

Authors

  • G. Parimala Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India
  • Kayalvizhi R. Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

Keywords:

IDS, adaptive pelican optimization algorithm, enhanced bi-directional long short term memory, malicious attack and feature selection

Abstract

Technology is advancing quickly, which not only makes life easier but also gives rise to various security problems. As the Internet has grown over time, so has the number of online attacks. To avoid the attacks, IDS (IDS) is developed. Nowadays, a lot of machine learning methods have been developed to detect attack. Although these jobs, decides the right feature and improving classification accuracy remains a challenging task. Therefore, in this paper, automatic attack detection is proposed. The presented approach is contains three stages namely, pre-processing, feature selection and classification.  In pre-processing, the redundant and missing data are removed. After the pre-processing, important features are selected from each records using adaptive pelican optimization algorithm (APO). Then, the certain attributes are fed to the enhanced bi-directional long short term memory (EBi-LSTM). The efficiency of presented technique is discussed based on different metrics.

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References

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Published

10.11.2023

How to Cite

Parimala, G. ., & R., K. . (2023). Automatic Intrusion Detection Using Optimal Features with Adaptive Bi-Directional Long Short Term Memory. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 710–716. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3856

Issue

Vol. 12 No. 4s (2024)

Section

Research Article

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