Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

A modular traffic sampling architecture: bringing versatility and efficiency to massive traffic analysis

The massive traffic volumes and heterogeneity of services in today's networks urge for flexible, yet simple measurement solutions to assist network management tasks, without impairing network performance. To turn treatable tasks requiring traffic analysis, sampling the traffic has become mandatory, triggering substantial research in the area. Despite that, there is still a lack of an encompassing solution able to support the flexible deployment of sampling techniques in production networks, adequate to diverse traffic scenarios and measurement activities. In this context, this article proposes a modular traffic sampling architecture able to foster the flexible design and deployment of efficient measurement strategies. The architecture is composed of three layers-management plane, control plane and data plane-covering key components to achieve versatile and lightweight measurements in diverse traffic scenarios and measurement activities. Each component of the architecture is described considering the different strategies, technologies and protocols that compose the several stages of a measurement process. Following the proposed architecture, a sampling framework prototype has been developed, providing a fair environment to assess and compare sampling techniques under distinct measurement scenarios, evaluating their performance in balancing computational burden and accuracy. The results have demonstrated the relevance and applicability of the proposed architecture, revealing that a modular and configurable approach to sampling is a step forward for improving sampling scope and efficiency.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio