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Youtube Spam Comments Detection
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| Author:
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B.NEELIMA , M.CHANDRA NAIK, T.CHANDRA SEKHAR RAO, B.NAGA RAJU, D.JEBAKUMAR IMMANUEL
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| Abstract:
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As Internet resources are used more often, network services are being attacked by hackers in creative ways. Network security is therefore becoming an essential component of the network substructure. Strong IDS (Intrusion Detection System) is required to efficiently and effectively identify such assaults. An IDS is a apparatus thoroughly examines apiece then respectively packet in in order to detect malevolent activity by dint of watching a system or network. IDS's primary function remains to spot unauthorized or unusual activity and alert the network administrator to it. IDS is thus a vital contrivance on behalf of the linkage overseer to protect the network from cooperation acknowledged and undiscovered. Effective intrusion detection systems may be implemented using machine learning techniques IDS. In this study, the categorization of the data was accomplished using four machine learning techniques: The NSL-KDD set of data be there used to train and assess these several machine learning models. Using feature selection techniques, undesirable and pointless characteristics from the dataset were eliminated. As a result, the dataset's dimensionality is reduced through article selection, which in turn lowers computing complexity. Three randomly chosen feature the suggested of data. The recommended approach includes a categorization.
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| Keyword:
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Spam Comments Detection, NSL-KDD Dataset, Support Vector Machine (SVM).
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2019.11.04.529
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| Download:
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Request For Article
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