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An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment

An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment

Partha Ghosh, Dipankar Sarkar, Joy Sharma, Santanu Phadikar
Copyright: © 2021 |Volume: 13 |Issue: 2 |Pages: 17
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799860358|DOI: 10.4018/IJDCF.2021030105
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MLA

Ghosh, Partha, et al. "An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment." IJDCF vol.13, no.2 2021: pp.77-93. http://doi.org/10.4018/IJDCF.2021030105

APA

Ghosh, P., Sarkar, D., Sharma, J., & Phadikar, S. (2021). An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment. International Journal of Digital Crime and Forensics (IJDCF), 13(2), 77-93. http://doi.org/10.4018/IJDCF.2021030105

Chicago

Ghosh, Partha, et al. "An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment," International Journal of Digital Crime and Forensics (IJDCF) 13, no.2: 77-93. http://doi.org/10.4018/IJDCF.2021030105

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Abstract

The present era is being dominated by cloud computing technology which provides services to the users as per demand over the internet. Satisfying the needs of huge people makes the technology prone to activities which come up as a threat. Intrusion detection system (IDS) is an effective method of providing data security to the information stored in the cloud which works by analyzing the network traffic and informs in case of any malicious activities. In order to control high amount of data stored in cloud, data is stored as per relevance leading to distributed computing. To remove redundant data, the authors have implemented data mining process such as feature selection which is used to generate an optimum subset of features from a dataset. In this paper, the proposed IDS provides security working upon the idea of feature selection. The authors have prepared a modified-firefly algorithm which acts as a proficient feature selection method and enables the NSL-KDD dataset to consume less storage space by reducing dimensions as well as less training time with greater classification accuracy.