State of the Art Review of Network Traffic Classification based on Machine Learning Approach
نویسندگان
چکیده
Network traffic classification is extensively required mainly for many network management tasks such as flow prioritization, traffic shaping/policing, and diagnostic monitoring. Similar to network management tasks, many network engineering problems such as workload characterization and modeling, capacity planning, and route provisioning also benefit from accurate identification of network traffic . This paper presents review on all the work done related to Network Traffic
منابع مشابه
Behavioral Analysis of Traffic Flow for an Effective Network Traffic Identification
Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a metho...
متن کاملLearning Document Image Features With SqueezeNet Convolutional Neural Network
The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...
متن کاملClassification of encrypted traffic for applications based on statistical features
Traffic classification plays an important role in many aspects of network management such as identifying type of the transferred data, detection of malware applications, applying policies to restrict network accesses and so on. Basic methods in this field were using some obvious traffic features like port number and protocol type to classify the traffic type. However, recent changes in applicat...
متن کاملCommunication-Aware Traffic Stream Optimization for Virtual Machine Placement in Cloud Datacenters with VL2 Topology
By pervasiveness of cloud computing, a colossal amount of applications from gigantic organizations increasingly tend to rely on cloud services. These demands caused a great number of applications in form of couple of virtual machines (VMs) requests to be executed on data centers’ servers. Some of applications are as big as not possible to be processed upon a single VM. Also, there exists severa...
متن کاملImproved Classification of Known and Unknown Network Traffic Flows Using Semi-supervised Machine Learning
Modern network traffic classification approaches apply machine learning techniques to statistical flow properties, allowing accurate classification even when traditional approaches fail. We base our approach to the task on a state-of-the-art semi-supervised classifier to identify known and unknown flows with little labelled training data. We propose a new algorithm for mapping clusters to class...
متن کامل