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An efficient SGM based IDS in cloud environment
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AI
DOI：10.1007/s00607-022-01059-4.png)
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Abstract
En 中文
Cloud computing is the sharing of remote access resources over the Internet. But with this comes an extensive risk of unauthorized access. Hence, for the security and privacy of the data, intrusion detection system (IDS) is required. IDS has to process a huge number of data with dimensions in search of intrusions. The more data it has to scan through, the more time it takes to detect an intrusion. Thus to reduce the dataset, feature selection (FS) is used where redundant data dimensions are discarded. In this paper, authors have proposed a novel Sage Grouse Mating algorithm which is to be implemented for FS in IDS. The proposed model was tested on NSL-KDD and Kyoto2006+ datasets. The proposed model increases the average accuracy of IDS up to 81.729% and reduces the number of features from 41 to 14 on NSL-KDD dataset. So, the experimental outcomes show that the proposed model enhanced the performance of IDS and outperforms all other metaheuristic algorithms compared in this paper. Therefore, it constitutes a robust IDS for Cloud Environment.
Keywords:
Cloud computing (CC)
Intrusion detection system (IDS)
Feature selection (FS)
Sage grouse mating (SGM)
NSL-KDD dataset
Kyoto dataset
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