Research and Application of Network Anomaly Traffic Detection System

As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research.

Internet has become an essential part of the daily life for billions of users worldwide, who are using a large variety of network services and applications everyday. However, there have been serious security problems and network failures that are hard to resolve, for example, botnet attacks, polymorphic worm/virus spreading, DDoS, and flash crowds. To address many of these problems, we need to have a network-wide view of the traffic dynamics, and more importantly, be able to detect traffic anomalies in a timely manner. Spatial analysis methods have been proved to be effective in detecting network-wide traffic anomalies that are not detectable at a single monitor. To our knowledge, Principle Component Analysis (PCA) is the best-known spatial detection method for the coordinated low-profile traffic anomalies. However, existing PCA-based solutions have scalability problems in that they require linear running time and space to analyze the traffic measurements within a sliding window, which makes it often infeasible to be deployed for monitoring large-scale high-speed networks. We propose a sketch-based streaming PCA algorithm for the network-wide traffic anomaly detection in a distributed fashion. Our algorithm only requires logarithmic running time and space at both local monitors and Network Operation Centers (NOCs), and can detect both high-profile and coordinated low-profile traffic anomalies with bounded errors.

We study the feasibility of developing a Communication Supervision Function for a 4G LTE wireless communications network system, to allow a vertical to monitor from its domain the Quality of Service (QoS) of the communication traversing the wireless domain. Communication supervision is performed by detecting traffic anomalies of a reference, healthy, transmission of packets uniformly spaced at intervals with ms resolution, and transmitted in uplink direction. Traffic at the base station is monitored with a LTE Medium Access Control (MAC) layer monitoring tool that aggregates traffic at intervals with seconds resolution. Measurements are performed in an operating LTE network. We use a deep learning method implementing a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), to determine if the traffic pattern is the healthy one, or it is anomalous, with missing packets and jitter. We identify key metrics in the monitoring data, that are selected as features in the RNN, which enable the detection of fine time resolution traffic anomalies hidden in the aggregated and coarse measurements reported by the monitoring tool. We find that applying the proposed approach, a vertical is able to determine whether the communication over the wireless network is healthy or anomalous. Finally, we discuss on the use of the proposed monitoring approach in 4G networks, and learning possibilities for 5G standardization in terms of monitoring metrics, features, monitoring resolution, service concepts, etc.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

We consider the network traffic monitoring issue, including traffic matrix estimation and anomaly detection problems. New model called structure regularized traffic monitoring (SRTM) is presented by incorporating the spatial and temporal properties of the traffic matrices, with the sparsity of the traffic volume anomalies. The proposed SRTM model adapts more naturally to the engineering nature of the real traffic data and imposes structural regularizations. This stems from the fact that the real traffic data is usually approximately instead of exactly low-rank and exhibits strong spatial and temporal dependencies. In addition, we take advantages of the sparsity of the traffic volume anomalies and impose sparse constraints on the variables to detect the traffic anomalies. Efficient algorithm that based on the proximal gradient and gradient descent methods are proposed to solve the SRTM optimization model. Experiments in synthetic networks are carried out to verify the feasibility and effectiveness of the proposed model.

With the rapid development of the Internet of Vehicles (IoV), traffic sign detection plays an indispensable role in advancing autonomous driving and intelligent transportation. However, current road traffic sign detection technologies face challenges in terms of information privacy protection, model accuracy verification, and result sharing. To enhance system sustainability, this paper introduces blockchain technology. The decentralized, tamper-proof, and consensus-based features of blockchain ensure data privacy and security among vehicles while facilitating trustworthy validation of traffic sign detection algorithms and result sharing. Storing model training data on distributed nodes reduces the system computational resources, thereby lowering energy consumption and improving system stability, enhancing the sustainability of the model. This paper introduces an enhanced GGS-YOLO model, optimized based on YOLOv5. The model strengthens the feature extraction capability of the original network by introducing a coordinate attention mechanism and incorporates a BiFPN feature fusion network to enhance detection accuracy. Additionally, the newly designed GGS convolutional module not only improves accuracy but also makes the model more lightweight. The model achieves an enhanced detection accuracy rate of 85.6%, with a reduced parameter count of 0.34×107. In a bid to broaden its application scope, we integrate the model with blockchain technology for traffic sign detection in the IoV. This method demonstrates outstanding performance in traffic sign detection tasks within the IoV, confirming its feasibility and sustainability in practical applications.

A mechanism to effectively detect malicious traffic in the present context where new cyber criminals and threatening actors are emerging every day, has become a compelling need. These invaders use overwhelming tactics that mask the nature of attacks and make bad acts seem innocuous. A growing number of trustworthy electronic systems and facilities have been introduced with the fast development of pervasive digital technologies. However threats to cyber-security continue to grow, posing hindrance in the efficient use of digital services. The detection and classification of malicious traffic due to security threats can be done by an efficacious traffic detection approach. The development of a smart, precise malicious traffic detection system has therefore become a subject of extensive research. Current traffic detection systems are typically employed in conventional network traffic detection. These systems sometimes face failure and cannot recognize many known or modern security threats. This is because they rely on conventional algorithms which focus less on precise selection and classification of functions. As a result, several well-known traffic signatures remain unidentified and latent. Hence, there is a need to evaluate each significant malicious traffic detection system based on the performance of the system. In this research work, the author has used the Fuzzy AHP methodology which is designed to address the issues related to the vagueness, uncertainties and total awareness of languages. In addition, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was implemented in order to assess the order of preference. Furthermore, the Multi-Criteria Decision-Making (MCDM) method was used for classifying the impact of the alternatives according to their overall performance. The study’s conclusive evaluations will be a corroborative reference for the practitioners working in the domain of assessing and selecting the most effective traffic detection approach for more reliable, efficient and systematic design.

Detecting anomalies in the Traffic Systems could be very useful for the analysis of traffic rule violation, fault detection and other traffic-related issues. In this paper trajectory-based anomaly detection using spatial temporal analysis, K-means, linear regression, z score and Hierarchical temporal memory clustering algorithm are analyzed. The spatial localization of an object is considered as an event. Traffic anomaly detection rules are formulated in three levels: Point anomaly, Sequential anomaly and Co-occurrence anomaly. This paper analyses the performance of various traffic anomaly detection methodologies in terms of accuracy to reduce false alarm.

Road traffic, by dimensions, weights and speeds of vehicles which is moving is a danger that requires precise deployment rules, advanced technical systems for safe movement, an appropriate infrastructure etc. Road safety is an issue of interest to all citizens, each of which can contribute to improving safety on public roads. Although the measures taken in this area have proven, road accidents remain unacceptably high. One of the main causes of serious accidents is the failure of its speed and road weather conditions. For this reason in this paper we present an electronic road traffic information and monitoring system. The system consists of a traffic information system and detection equipment and weather monitoring, video imaging to speeding vehicles and the weight permitted for movement on public roads.

This paper proposes a real-time traffic sign detection system. In order to extract accurately the shape feature of the traffic sign, reduce the computational load and further improve the traffic sign detection rate, a novel approach based on surround suppression of texture edges and cascade detector is presented. The principle of surround suppression mainly adds a computational step to the canny edge detector. This operator responds strongly to isolated lines and edges, region boundaries, and object contours. To discriminate a set of traffic signs from the background, a boosted cascade detector is used. In this boosted cascade detector, the adaboost algorithm is applied to select a small number of critical visual features and yield extremely efficient classifiers. Experiments show that our method is efficient to detect traffic signs and can be used in real-time traffic sign detection system.

This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

An improved quasi-z-source dual-inverter for open-end winding induction motor drives is presented in the paper. The improved quasi-z-source dual-inverter can not only realize boost/buck function, but also suppress DC-link bus overshoot voltage, which is suitable for medium/high-power level. The topology allows shoot-through state, so that the dead-time effect cannot be existed. The dual-inverter belongs to the three-level drive system. The utilization rate of DC-link bus voltage is accordingly improved. The proposed topology has high reliability, strong fault tolerance and single-stage boost/buck voltage function, bidirectional energy flow, except simple structure and low cost of open-end winding induction motor. Due to strong fault tolerance and boost function in the proposed topology, the dynamic performance of the drive system can be significantly improved. The operating mode and the modulation method are analyzed in this paper. The simulation and experimental results can verify the feasibility of the improved topology for the motor drives.

With advancements in autonomous driving, demand for stringent and computationally efficient traffic sign detection systems has increased. However, bringing such a system to a deployable level requires handling critical accuracy and processing speed issues. A focal loss-based single-stage object detector, i.e RetinaNet, is used as a trade-off between accuracy and processing speed as it handles the class imbalance problem of the single-stage detector and is thus suitable for traffic sign detection (TSD). We assessed the detector’s performance by combining various feature extractors such as ResNet-50, ResNet-101, and ResNet-152 on three publicly available TSD benchmark datasets. Performance comparison of the detector using different backbone includes evaluation parameters like mean average precision (mAP), memory allocation, running time, and floating-point operations. From the evaluation results, we found that the RetinaNet object detector using the ResNet-152 backbone obtains the best mAP, while that using ResNet-101 strikes the best trade-off between accuracy and execution time. The motivation behind benchmarking the detector on different datasets is to analyse the detector’s performance on different TSD benchmark datasets. Among the three feature extractors, the RetinaNet model trained using the ResNet-50 backbone is an excellent model in memory consumption, making it an optimal choice for low-cost embedded devices deployment.

There is a need for advanced traffic management systems (ATMS) on a local and network level to effectively utilize today`s transportation system. An automated operating system, with round the clock incident detection, would help to initiate the required action on time in case of an accident and would minimize the effect of the incident. One of the key elements of ATMS is the vehicle detection unit. The most commonly used inductive loop detectors have a relatively short life span and frequently malfunction. Any maintenance of loop detectors requires road closures. Also, safety of the crew is an issue. An overview of currently available technologies for traffic control and monitoring is presented, with special emphasis on systems that can be installed and maintained without significantly interfering with the traffic. The paper presents the available technologies, provides comparative information about them, and discusses the technical and conceptual problems typically arising during the deployment. The paper describes video detection systems that currently seem to be the most attractive alternative to the traditional intrusive methods. The new perspectives for traffic monitoring and management not available so far are drawn. The typical architecture of these systems is described, and a few popular video systems aremore » presented in some detail. The most critical deployment issues, such as the choice of the equipment, are described, the location of the sensors is discussed, and data transmission problems are considered.« less

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur and evaluate the security. However, the number of traffic anomalies is extremely limited, so there is a severe over-fitting problem when using traditional deep learning methods. In order to solve the problem above, we propose a similarity metric Convolutional Neural Network (CNN) based on a channel attention model for traffic anomaly detection task. The method mainly includes (1) A Siamese network with a hierarchical attention model by word embedding so that it can selectively measure similarities between anomalies and the templates. (2) A deep transfer learning method can automatically annotate an unlabeled set while fine-tuning the network. (3) A background modeling method combining spatial and temporal information for anomaly extraction. Experiments show that the proposed method is three percentage points higher than deep convolutional generative adversarial network (DCGAN) and five percentage points higher than AutoEncoder on the accuracy. No more time consumption is needed for the annotation process. The extracted candidates can be classified correctly through the proposed method.