Abstract Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple fast structure. Aiming at the shortcomings naive Bayes algorithm, this paper uses feature weighting Laplace calibration to improve it, obtains improved algorithm. Through numerical simulation, it found that when sample size large, accuracy more than 99%, very stable; attri...

Abstract To resolve the issues of low accuracy, weak universality, and easy invasion privacy in traditional encryption traffic classification methods, an method based on a convolutional neural network is offered. Firstly, according to packet size time message net traffic, original transformed into two-dimensional picture avoid relying payload violate privacy, then model embedded. The Inception ...

Network Traffic Classification (NTC) has become an important feature in various network management operations, e.g., Quality of Service (QoS) provisioning and security services. Machine Learning (ML) algorithms as a popular approach for NTC can promise reasonable accuracy classification deal with encrypted traffic. However, ML-based techniques suffer from the shortage labeled traffic data which...

We contribute to an improved understanding of Internet traffic characteristics by measuring and analyzing modern Internet backbone data. We start the thesis with an overview of several important considerations for passive Internet traffic collection on large-scale network links. The lessons learned from a successful measurement project on academic Internet backbone links can serve as guidelines...

Network traffic anomalies detection and characterization has been a hot topic of research for many years. Although the field is very advanced in the detection of network traffic anomalies, accurate automated classification is still a very challenging and unmet problem. This paper presents a new algorithm for automated classification of network traffic anomalies. The algorithm relies on three st...