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

	Text Abstrak_JATIT.pdf Download (499kB)
	Text Turnitin - JATIT.pdf Download (1MB)

Search this title on : |

Abstract

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.

Export Record

ASCII Citation Atom BibTeX Dublin Core EP3 XML EndNote HTML Citation JSON METS MODS MPEG-21 DIDL OpenURL ContextObject OpenURL ContextObject in Span RDF+N-Triples RDF+N3 RDF+XML Refer Reference Manager Simple Metadata

Download Statistics

Downloads over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Actions (login required)

View Item