Intrusion Detection System Based on Frequent Pattern Mining
| Main Authors: | Khin Moh Moh Aung, Nyein Nyein Oo, Myo Min Than |
|---|---|
| Format: | Proceeding eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2014
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/3258093 |
Daftar Isi:
- Due to the dramatically increment of internet usage, users are facing various attacks day by day. Consequently, the research area for intrusion detection must be fresh with new challenges. Intrusion detection system includes identifying a set of malicious actions that compromise the integrity, confidentiality, and availability of information resources. The major contribution is to apply data mining approach for network intrusion detection system. Among the several features of data mining, association rules mining,FP- growth algorithm, is used to find out the frequent itemsets of incoming packets database. Based on these itemsets, anomaly detection is added. The system will predict whether the incoming data packet is normal or attack. The performance of proposed system is tested by using KDD-99 datasets.