The Library
An anomaly-based IDS framework using centroid-based classification
Tools
Lin, Iuon-Chang, Chang, Ching-Chun and Peng, Chih-Hsiang (2022) An anomaly-based IDS framework using centroid-based classification. Symmetry, 14 (1). e105. doi:10.3390/sym14010105 ISSN 2073-8994.
|
PDF
WRAP-an-anomaly-based-IDS-centroid-based-classification-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (22Mb) | Preview |
Official URL: https://doi.org/10.3390/sym14010105
Abstract
Botnet is an urgent problem that will reduce the security and availability of the network. When the bot master launches attacks to certain victims, the infected users are awakened, and attacks start according to the commands from the bot master. Via Botnet, DDoS is an attack whose purpose is to paralyze the victim’s service. In all kinds of DDoS, SYN flood is still a problem that reduces security and availability. To enhance the security of the Internet, IDS is proposed to detect attacks and protect the server. In this paper, the concept of centroid-based classification is used to enhance performance of the framework. An anomaly-based IDS framework which combines K-means and KNN is proposed to detect SYN flood. Dimension reduction is designed to achieve visualization, and weights can adjust the occupancy ratio of each sub-feature. Therefore, this framework is also suitable for use on the modern symmetry or asymmetry architecture of information systems. With the detection by the framework proposed in this paper, the detection rate is 96.8 percent, the accuracy rate is 97.3 percent, and the false alarm rate is 1.37 percent.
Item Type: | Journal Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
|||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
SWORD Depositor: | Library Publications Router | |||||||||
Library of Congress Subject Headings (LCSH): | Computer networks -- Security measures , Computer viruses, Malware (Computer software), Malware (Computer software) -- Prevention, Computer viruses -- Prevention -- Software, Denial of service attacks | |||||||||
Journal or Publication Title: | Symmetry | |||||||||
Publisher: | MDPI | |||||||||
ISSN: | 2073-8994 | |||||||||
Official Date: | 8 January 2022 | |||||||||
Dates: |
|
|||||||||
Volume: | 14 | |||||||||
Number: | 1 | |||||||||
Article Number: | e105 | |||||||||
DOI: | 10.3390/sym14010105 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 27 January 2022 | |||||||||
Date of first compliant Open Access: | 31 January 2022 | |||||||||
RIOXX Funder/Project Grant: |
|
|||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year