Research Article
Implementation of CM-SPADE Algorithm In Building Denial of Service Detection System Model Using Snort
@INPROCEEDINGS{10.4108/eai.12-10-2019.2296332, author={Eddy Prasetyo Nugroho and Rani Megasari and Enjun Junaeti and Samekto Rinekso Pribadi}, title={Implementation of CM-SPADE Algorithm In Building Denial of Service Detection System Model Using Snort}, proceedings={Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia}, publisher={EAI}, proceedings_a={MSCEIS}, year={2020}, month={7}, keywords={intrusion detection system data mining cm-spade kdd cup ’99 dataset snort}, doi={10.4108/eai.12-10-2019.2296332} }- Eddy Prasetyo Nugroho
Rani Megasari
Enjun Junaeti
Samekto Rinekso Pribadi
Year: 2020
Implementation of CM-SPADE Algorithm In Building Denial of Service Detection System Model Using Snort
MSCEIS
EAI
DOI: 10.4108/eai.12-10-2019.2296332
Abstract
Along with the increasing use of computers, business activities and data storage also use it. The increasing importance of the computer raises challenges and risks, especially in security of the computer system and network. Various ways have been done to improve computer network security, one of which is using the Intrusion Detection System (IDS). To maximize the IDS detection function that is able to detect unknown attacks, a data mining approach is used to create an IDS that uses anomaly-based detection techniques. In this study, a sequential pattern mining technique, the CM-SPADE algorithm, is used to generate IDS rules that can detect DoS attacks. Modeling and rules are made by applying the CM-SPADE algorithm to the KDD Cup 1999 data set. The results reveal that the implementation of the CM-SPADE algorithm is able to produce IDS rules that can detect DoS attacks with an accuracy rate of 97.976%.