Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering

• Autor(a) principal: Guimaraes, Raniere Rocha
• Data de Publicação: 2019
• Outros Autores: Passos Jr, Leandro A., Holanda Filho, Raimir, Albuquerque, Victor Hugo C. de, Rodrigues, Joel J. P. C., Komarov, Mikhail M., Papa, Joao Paulo [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/MNET.2018.1800151; http://hdl.handle.net/11449/186701

Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to fine-tune the OPF classifier in the context of anomaly detection in wireless sensor networks.