International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Intrusion Detection System on Cloud Computing using Ensemble SVM

Author(s) R.Hari Krishna, Pallipamula Vijaya Bhaskar, Prasritha Narahari, Meghana Nalluri, Mohith Ram Mallapureddy
Country India
Abstract This study introduces a specialized Intrusion Detection System (IDS) designed specifically for cloud computing environments. By leveraging the UNSW_NB15 dataset, it employs feature selection techniques such as SelectKBest and ANOVA to extract relevant features, thereby improving the overall performance of the model. The IDS framework encompasses data preprocessing, Ensemble SVM model training, and performance evaluation utilizing standard metrics. The key methodology revolves around training SVM models using bagging and boosting techniques on preprocessed data, resulting in resilient intrusion detection models. These models are subsequently utilized to determine whether a given set of input features signifies a network intrusion. Through experimental analysis, the research demonstrates the system's efficacy in accurately detecting network intrusions, shedding light on its robustness and dependability.
Keywords UNSW_NB15, bagging, boosting, Ensemble SVM, ANOVA, SelectKBest
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-13
Cite This Intrusion Detection System on Cloud Computing using Ensemble SVM - R.Hari Krishna, Pallipamula Vijaya Bhaskar, Prasritha Narahari, Meghana Nalluri, Mohith Ram Mallapureddy - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.17212
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.17212
Short DOI https://doi.org/gtqxrk
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E-ISSN 2582-2160
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