Smart City Gnosys
Smart city article details
| Title | Lnad: Towards Lightweight Network Anomaly Detection In Software-Defined Networking |
|---|---|
| ID_Doc | 35411 |
| Authors | Cui Y.; Qian Q.; Xing H.; Li S. |
| Year | 2020 |
| Published | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 |
| DOI | http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00113 |
| Abstract | As an emerging architecture, Software-Defined Networking (SDN) is suffering from a lot of security issues. Network anomaly detection technology plays a crucial role in addressing SDN security issues. In SDN, the network status can be denoted by the packets or flow entries. Hence, the existing network anomaly detection methods are commonly designed based on inspecting packets or flow entries. Subsequently, these anomaly detection methods have to collect packets or flow entries from the underlying switches. More seriously, some anomaly detection methods even need to add extra modules to the switches. Unlike these existing network anomaly detection methods, this work proposes a lightweight network anomaly detection method. The main design principle of the proposed method is mining the inherent OpenFlow messages in SDN to represent the network status and further detect the network anomaly. The proposed method does not need to collect any additional messages from the underlying switches or add any additional modules to the underlying switches. The evaluation results demonstrate that the proposed network anomaly detection method can afford high detection accuracy and reduce the overhead of SDN controller. © 2020 IEEE. |
| Author Keywords | network anomaly detection; OpenFlow; SDN; self-inspection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
9622  | 0.882 | Jung O.; Smith P.; Magin J.; Reuter L. | Anomaly Detection In Smart Grids Based On Software Defined Networks | SMARTGREENS 2019 - Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems (2019) |
| 53805 | 0.872 | Ujjan R.M.A.; Pervez Z.; Dahal K. | Suspicious Traffic Detection In Sdn With Collaborative Techniques Of Snort And Deep Neural Networks | Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (2019) |
| 32590 | 0.85 | Schmitt S.; Kandah F.I.; Brownell D. | Intelligent Threat Hunting In Software-Defined Networking | 2019 IEEE International Conference on Consumer Electronics, ICCE 2019 (2019) |