Slamet, Slamet and Izzeldin, Ibrahim Mohamed (2021) Network intrusions classification using data mining approaches. Journal of Theoretical and Applied Information Technology, 99 (7). pp. 1679-1692. ISSN 1992-8645 (print); 817-3195 (online). (Published)
|
Pdf
Network intrusions classification using data mining approaches.pdf Download (499kB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Anomaly; Classification; Intrusion; Misuse; Snort |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering Institute of Postgraduate Studies |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 06 Jul 2023 07:46 |
| Last Modified: | 13 Jul 2023 01:46 |
| URI: | http://umpir.ump.edu.my/id/eprint/37961 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |