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 applications make these features imperfect for such tasks. As a remedy, Network Traffic Classification using machine learning techniques is now evolving. In this article, a new semi-supervised learning is proposed which utilizes clustering algorithms and label propagation techniques. The clustering part is based on graph theory and minimum spanning tree algorithm. In the next level, some pivot data instances are selected for the expert to vote for their classes, and the identified class labels will be used for similar data instances with no labels. In the last part, the decision tree algorithm is used to construct the Classification model. The results show that the proposed method has a precise and accurate performance in Classification of encrypted Traffic for the Network applications. It also provides desirable results for plain un-encrypted Traffic Classification, especially for unbalanced streams of data.

Traffic road sign detection is important to a robotic vehicle that automatically drives on roads. As the colors of most Traffic road signs are blue and red, in this paper, we use Hue- Saturation- Intensity (HSI) color space for color based segmentation at first. Using important geometrical features, the road signs are detected perfectly. After segmentation, it turns to classify every detected road signs. For this purpose, we employ and compare the performance of three classifiers; they are distance to border (DTB), FFT sample of signature, and code matrix. In this work, we use the code matrix as an efficient classifier for the first time. Although the achieved accuracy by code matrix is greater than the two referred classifiers in average, the main advantage is simplicity and so less computational cost.

In this study, we aim to use new deep-learning tools and convolutional neural Networks for Traffic analysis. ResNeXt architecture, one of the most potent architectures and has attracted much attention in various fields, has been proposed to examine the scene, and classify it into three categories: cars, bikes (bicycles/motorcycles), and pedestrians. Previous studies have focused more on one type of Classification and reported only human-facial recognition or vehicle detection. In contrast, the proposed method uses precise architecture to perform the Classification of three classes. The proposed plan has been implemented in several steps: the first stage is to divide the critical objects. In the next step, the characteristics of the obtained objects are extracted to classify the process into three classes. Experiments have been conducted on different and essential datasets such as high-Traffic, low-quality, real-time scenes. Essential evaluation criteria such as accuracy, sensitivity, and specificity show that the performance of the proposed method has improved compared to the methods being compared. The accuracy criterion reached more than 92%, sensitivity about 89%, and specially to 90.25%. The proposed method can be used to implement intelligent cities, public safety, and metropolitan decisions and use the results in urban management, predictive modeling of lost data management, sequential data management, and generalizability.

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 method to identify Network Traffics. In this method, for cleaning and preparing data, we perform effective preprocessing approach. Then effective features are extracted using the behavioral analysis of application. Using the effective preprocessing and feature extraction techniques, this method can effectively and accurately identify Network Traffics. For this purpose, two Network Traffic databases namely UNIBS and the collected database on router are analyzed. In order to evaluate the results, the accuracy of Network Traffic identification using proposed method is analyzed using machine learning techniques. Experimental results show that the proposed method obtains an accuracy of 97% in Network Traffic identification.