Name
Abstract | ||
|---|---|---|
Data mining techniques such as contrast pattern mining provide a promising approach to detecting and characterizing changes in network traffic. However, a major challenge for network managers is how to prioritize their analysis of these changes, without being overwhelmed by uninformative patterns. In particular, some changes in traffic occur on a regular basis, such as system backups, and it is important to filter out these rare recurring events, so that network managers can focus on new events. In this paper we address the problem of identifying rare recurring events in network traffic, and we propose a novel solution to detecting new events based on the approach of mining second order contrast patterns. Based on an empirical evaluation using a variety of real traffic sources, we show that our method can achieve high accuracy and F1-Score in detecting new events. Our work demonstrates the importance of higher order contrast pattern mining in practice, and provides an effective method for finding such higher order patterns in large datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533918 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
contrast pattern mining, emerging pattern mining, higher order contrast patterns | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elaheh Alipour Chavary | 1 | 0 | 0.34 |
| Sarah M. Erfani | 2 | 3 | 3.07 |
| Christopher Leckie | 3 | 2422 | 155.20 |