Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis

Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of the execution time of CNN training, and GPUs are commonly used to accelerate these layer workloads. GPU design optimization for efficient CNN training acceleration requires the accurate modeling of how their performance improves when computing and memory resources are increased. We present DeLTA, the first analytical model that accurately estimates the traffic at each GPU memory hierarchy level, while accounting for the complex reuse patterns of a parallel convolution algorithm. We demonstrate that our model is both accurate and robust for different CNNs and GPU architectures. We then show how this model can be used to carefully balance the scaling of different GPU resources for efficient CNN performance improvement

Deep learning for the analysis of network traffic measurements

The application of machine learning models to the analysis of network traffic measurements has largely increased in recent years. In the networking domain, shallow models are usually applied, where a set of expert handcrafted features are needed to fix the data before training. There are two main problems associated with this approach: firstly, it requires expert domain knowledge to select the input features, and secondly, different sets of custom-made input features are generally needed according to the specific target (e.g., network security, anomaly detection, traffic classification). On the other hand, the power of machine learning models using deep architectures (i.e., deep learning) for networking has not been yet highly explored. These models have had huge success in various domains, notably in computer vision, natural language processing, machine translation, and more recently in gaming. The main goal of this work is to explore the power of deep learning models to enhance the analysis of network tra c measurements. To this end, the specific problem of detection and classi cation of network attacks is studied. As a major advantage with respect to the state-of-the-art in the field, the evaluation of different raw-traffic input representations, including packet and ow-level ones, is considered. Different deep learning architectures are explored, including convolutional neural networks and long short-term memory recurrent neural networks as core layers. In addition, three different datasets are crafted from publicly available network traffic captures and used for calibrating the considered input representations, as well as training and validating the proposed models. Different deep learning models are compared to a random forest model - commonly accepted as a highly accurate model for network traffic analysis, using the same raw input representations. In the malware detection task, a detection accuracy of 77.6% and 98.5% was achieved for packet and ow input representations respectively. For the malware classification task, an overall accuracy of 76.5% was achieved. In all evaluation tasks, the proposed deep learning models outperform the random forest ones. These initial results suggest that deep learning can be used to enhance malware detection without requiring expert domain knowledge to handcraft input features, opening the door to a broad set of potential applications for deep learning in networking

Using deep learning to classify community network traffic

Traffic classification is an important aspect of network management. This aspect improves the quality of service, traffic engineering, bandwidth management and internet security. Traffic classification methods continue to evolve due to the ever-changing dynamics of modern computer networks and the traffic they generate. Numerous studies on traffic classification make use of the Machine Learning (ML) and single Deep Learning (DL) models. ML classification models are effective to a certain degree. However, studies have shown they record low prediction and accuracy scores. In contrast, the proliferation of various deep learning techniques has recorded higher accuracy in traffic classification. The Deep Learning models have been successful in identifying encrypted network traffic. Furthermore, DL learns new features without the need to do much feature engineering compared to ML or Traditional methods. Traditional methods are inefficient in meeting the demands of ever-changing requirements of networks and network applications. Traditional methods are unfeasible and costly to maintain as they need constant updates to maintain their accuracy. In this study, we carry out a comparative analysis by adopting an ML model (Support Vector Machine) against the DL Models (Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU to classify encrypted internet traffic collected from a community network. In this study, we performed a comparative analysis by adopting an ML model (Support vector machine). Machine against DL models (Convolutional Neural networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU) and to classify encrypted internet traffic that was collected from a community network. The results show that DL models tend to generalise better with the dataset in comparison to ML. Among the deep Learning models, the hybrid model outperformed all the other models in terms of accuracy score. However, the model that had the best accuracy rate was not necessarily the one that took the shortest time when it came to prediction speed considering that it was more complex. Support vector machines outperformed the deep learning models in terms of prediction speed