An Abnormal Network Traffic Detection Scheme Using Local Outlier Factor in SDN
| Autor: | Nguyen Danh Nghia, Nguyen Minh Hieu, Pham Huy Hung, Nguyen Tai Hung, Dinh Khac Tuyen, Nguyen Huu Thanh, Nguyen Ngoc Tuan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Network architecture
Local outlier factor Computer science business.industry 05 social sciences Testbed Process (computing) 050801 communication & media studies computer.software_genre 0508 media and communications Software 0502 economics and business 050211 marketing Anomaly detection Data mining State (computer science) business computer |
| Zdroj: | 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). |
| DOI: | 10.1109/icce48956.2021.9352123 |
| Popis: | Anomaly detection is an important step in the process of attack detection and mitigation. As the first step before attack detection mechanisms are used, it is to be decided if the network is in normal or abnormal state by anomaly traffic detection mechanisms. Software Define Network is a network architecture widely used recently as it is a flexible and open architecture allowing deploying on-demand functionalities into the network, including complex security and management algorithms. In this paper, we propose a machine learning algorithm based on Local outlier Factor (LoF) in Software Define Networking to detect abnormal traffic. LoF is a light-weight machine learning algorithm that requires minimal network resources. By setting up a testbed, we implement the proposed algorithm and evaluate its performance. The results show that the algorithm can perform in real-time and detect anomaly traffic in high accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|