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Fuzzy Based Latent Dirichlet Allocation for Intrusion Detection in Cloud Using ML

S. Ranjithkumar1,*, S. Chenthur Pandian2

1 Sri Ramakrishan Engineering College, Coimbatore, 641022, India
2 SNS College of Technology, Coimbatore, 641035, India

* Corresponding Author: S. Ranjithkumar. Email: email

Computers, Materials & Continua 2022, 70(3), 4261-4277. https://doi.org/10.32604/cmc.2022.019031

Abstract

The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system (IDS). IDS are considered as an essential factor to fulfill security requirements. Recently, there are diverse Machine Learning (ML) approaches that are used for modeling effectual IDS. Most IDS are based on ML techniques and categorized as supervised and unsupervised. However, IDS with supervised learning is based on labeled data. This is considered as a common drawback and it fails to identify the attack patterns. Similarly, unsupervised learning fails to provide satisfactory outcomes. Therefore, this work concentrates on semi-supervised learning model known as Fuzzy based semi-supervised approach through Latent Dirichlet Allocation (F-LDA) for intrusion detection in cloud system. This helps to resolve the aforementioned challenges. Initially, LDA gives better generalization ability for training the labeled data. Similarly, to handle the unlabelled data, Fuzzy model has been adopted for analyzing the dataset. Here, pre-processing has been carried out to eliminate data redundancy over network dataset. In order to validate the efficiency of F-LDA towards ID, this model is tested under NSL-KDD cup dataset is a common traffic dataset. Simulation is done in MATLAB environment and gives better accuracy while comparing with benchmark standard dataset. The proposed F-LDA gives better accuracy and promising outcomes than the prevailing approaches.

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APA Style
Ranjithkumar, S., Pandian, S.C. (2022). Fuzzy based latent dirichlet allocation for intrusion detection in cloud using ML. Computers, Materials & Continua, 70(3), 4261-4277. https://doi.org/10.32604/cmc.2022.019031
Vancouver Style
Ranjithkumar S, Pandian SC. Fuzzy based latent dirichlet allocation for intrusion detection in cloud using ML. Comput Mater Contin. 2022;70(3):4261-4277 https://doi.org/10.32604/cmc.2022.019031
IEEE Style
S. Ranjithkumar and S.C. Pandian, "Fuzzy Based Latent Dirichlet Allocation for Intrusion Detection in Cloud Using ML," Comput. Mater. Contin., vol. 70, no. 3, pp. 4261-4277. 2022. https://doi.org/10.32604/cmc.2022.019031



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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