Autonomic communication system based on cognitive techniques

Aguilar, José; Torres, José F.; Aguilar, Kristell

doi:10.3233/kes-170373

Cited by 2 publications

(1 citation statement)

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“…• Finally, ML solutions that allow considering the dynamism of the network must be deployed. The development of autonomic architectures [195], [196], [197], allowing context adaptation (behavioral or structural adaptation), can be applied by using ML models.…”

Section: Discussionmentioning

confidence: 99%

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

Pacheco

Expósito

Gineste³

et al. 2019

IEEE Commun. Surv. Tutorials

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Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application's name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine Learning gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. Machine Learning is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using Machine Learning techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for Machine Learning based traffic classification.

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Section: Discussionmentioning

confidence: 99%