Network Intrusions Classification Using Data Mining Approaches

Slamet, Slamet and Mohamed, Izzeldin Ibrahim (2021) Network Intrusions Classification Using Data Mining Approaches. Journal of Theoretical and Applied Information Technology (JATIT), 99 (7). pp. 1679-1692. ISSN 1992-8645

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Intrusion Detection System has an important task in detecting threats or attacks in the computer networks. Intrusion Detection System (IDS) is a network protection device used to identify and check data packets in network traffic. Snort is free software used to detect attacks and protect computer networks. Snort can only detect misuse attacks, whereas to detect anomaly attacks using Bayes Network, Naive Bayes, Random Tree, LMT and J-48 Classification Method. In this paper, the experimental study uses the KDDCUP 99 dataset and the dataset taken from Campus Network. The main objective of this research is to detect deceptive packets that pass computer network traffic. The steps taken in this study are data preparation, data cleaning, dataset classification, feature extraction, rules snort for detecting, and detecting packet as an attack or normal. The result of the proposed system is an accurate detection rate.

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Item Type: Article
Uncontrolled Keywords: Classification, Intrusion, Snort, Anomaly, Misuse
Dewey Decimal Classification: 000 – Computer science, information & general works > 000 Computer science, knowledge & systems > 000 Computer science, information & general works
Divisions: Perpustakaan > Journals
Depositing User: Agung P. W.
Date Deposited: 25 Sep 2023 08:29
Last Modified: 25 Sep 2023 08:29

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