Network Intrusion Detection Systems Analysis using Frequent Item Set Mining Algorithm FP-Max and Apriori

• Published in: Procedia computer science 2017, Vol.124, p.751-758
• Main Authors: Hidayanto, Bekti Cahyo, Muhammad, Rowi Fajar, Kusumawardani, Renny P, Syafaat, Achmad
• Format: Article
• Language: English
• Subjects: Apriori; FP-MAX; Frequent Item Set Mining; Internet Attack; Intrusion Detection Systems; SNORT
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2017.12.214

Within the fast growing of internet user and technology in Indonesia, thus threat coming from internet is raising. The threat is common for all user in the world. Therefore, the malware has growth rapidly and the behavior is becoming more advanced. From these problem, it is important to know, how the malware is growing and how the characteristics about malware attack in Indonesia. This research aim used the data source taken from Intrusion Detection Systems sensor from Id-SIRTII/CC, Ministry Information and Communication Indonesia. This research finds for any type of attack which frequently occurred using Frequent Item Set Mining. Therefore, data will be visualized for giving the better analysis result and giving the overview about the internet security condition in Indonesia in 2013. In minimum support 95% in frequent item set mining (both Apriori and FP-Max), we found that malware frequently occurred are SQL attack, Malware Virus DNS and DoS. The largest malware in our data only have slightly less than 80% than another pattern that have more than 90% value of support.