Journal of Decision Engineering

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

---

Abstract- Skype is one of the most powerful and high-quality chat tools that allows its users to use of many services such as: transferring audio, sending messages, video conferencing and audio for free. Skype traffic has a lot of Internet traffic. Hence, Internet service providers need to identify traffic to do the quality of service and network management. On the other hand, Skype developers have been attempting to encrypt Skype traffic because traditional methods like port-based and deep packet inspection can not identify Skype traffic. As a result, we use statistical methods to identify these types of traffic. Hence, in this study, we use unsupervised machine learning methods, which is a statistical method, to separate the various services of Skype. The algorithms used in this work are K-Means, EM and Density-based. The results show that the EM algorithm has better performance than other algorithms. Also, by comparing the proposed strategy with previous work, results indicate that algorithms detect traffic better than other

Keywords: internet traffic, Skype, identify encrypted flow, clustering algorithms

Full-Text [PDF 711 kb]   (677 Downloads)    

Add your comments about this article : Your username or Email:

---

---