Real-Time Traffic Classification Based on Statistical and Payload Content Features

• Main Authors: Dehghani, Fereshte, Movahhedinia, Nasser, Khayyambashi, Mohammad Reza, Kianian, Sahar
• Format: Conference Proceeding
• Language: English
• Subjects: Application software; Communication system traffic control; Cryptography; Delay; Inspection; Intrusion detection; Machine learning; Payloads; Quality of service; Telecommunication traffic
• DOI: 10.1109/IWISA.2010.5473467

In modern networks, different applications generate various traffic types with diverse service requirements. Thereby the identification and classification of traffic play an important role for increasing the performance in network management. Primitive applications were using well-known ports in transport layer, so their traffic classification can be performed based on the port number. However, the recent applications progressively use unpredictable port numbers. Consequently the later methods are based on "deep packet inspection". Notwithstanding proper accuracy, these methods impose heavy operational load and are vulnerable to encrypted flows. The recent methods classify the traffic based on statistical packet characteristics. However, having access to a little part of statistical flow information in real-time traffic may jeopardize the performance of these methods. Regarding the advantages and disadvantages of the two later methods, in this paper we propose an approach based on payload content and statistical traffic characteristics with Naive Bayes algorithm for real-time network traffic classification. The performance and low complexity of the propose approach confirm its competency for real-time traffic classification.