Open AccessAn Improved Frequent Pattern Growth Based Approach to Intrusion Detection System Alert Aggregation
Author(s) -
Yun Sun,
Xiaomei Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012070
Subject(s) - intrusion detection system , computer science , data mining , alarm , association rule learning , false alarm , similarity (geometry) , anomaly based intrusion detection system , pattern recognition (psychology) , artificial intelligence , engineering , image (mathematics) , aerospace engineering
This paper introduces different approaches to intrusion detection system (IDS) alert aggregation and proposes an improved frequent pattern growth (FP-growth) algorithm for it. This approach can be divided into three parts, which are removal of noisy data, mining association rules and text similarity check. According to the experiment on Snort alarm dataset provided by an enterprise, all the association rules found by the proposed approach are valid. Therefore, compared with FP-growth algorithm, the proposed approach can increase the precision of the result and is useful for alert aggregation.