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"Communications traffic"
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Driver acceptance of new technology : theory, measurement and optimisation
\"Acceptance of new technology and systems by drivers is an important area of concern to governments, automotive manufacturers and equipment suppliers, especially technology that has significant potential to enhance safety. To be acceptable, new technology must be useful and satisfying to use. If not, drivers will not want to have it, in which case it will never achieve the intended safety benefit. Even if they have the technology, drivers may not use it if it is deemed unacceptable, or may not use it in the manner intended by the designer. At worst, they may seek to disable it\"-Provided by publisher.
Book
FARIMA model‐based communication traffic anomaly detection in intelligent electric power substations
The technological advances of intelligent electric substations have significantly improved the operational performance of power utilities by incorporating advanced monitoring and control functionalities. The data traffic patterns in substation communication network (SCN) need to be better understood to improve the SCN performance against different forms of cyber‐attacks. To this end, this study presents a fractional auto‐regressive integrated moving average (FARIMA)‐based threshold model to characterise the SCN traffic flow based on the IEC 61850 protocol and carry out anomaly detection. The performance of the proposed anomaly detection solution is assessed and validated through numerical analysis under the condition of the cyber storm based on the collected SCN data traffic from a real 110 kV substation, and the numerical results clearly confirmed its effectiveness.
Journal Article
Organizational Impact of Spatiotemporal Graph Convolution Networks for Mobile Communication Traffic Forecasting
Communication traffic prediction is of great guiding significance for communication planning management and improvement of communication service quality. However, due to the complex spatiotemporal correlation and uncertainty caused by the spatial topology and dynamic time characteristics of mobile communication networks, traffic prediction is facing enormous challenges. We propose a mobile traffic prediction method using dynamic spatiotemporal synchronous graph convolutional network (DSSGCN). DSSGCN has designed multiple components, which can effectively capture the heterogeneity in the local space-time map. More specifically, the network not only models the dynamic characteristics of nodes in the spatiotemporal graph of network traffic, but also captures the dynamic spatiotemporal characteristics of the edges of mobile service data with different time stamps. The outputs of these two components are fused by collaborative convolution to obtain the prediction results. Experiments on two ground truth mobile traffic datasets show that our DSSGCN model has good prediction performance.
Journal Article
Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.
Journal Article
Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
Advanced Driver Assistance Systems (ADASs) are crucial components of intelligent vehicles, equipped with a vast code base. To enhance the security of ADASs, it is essential to mine their vulnerabilities and corresponding exploitation methods. However, mining buffer overflow (BOF) vulnerabilities in ADASs can be challenging since their code and data are not publicly available. In this study, we observed that ADAS devices commonly utilize unencrypted protocols for module communication, providing us with an opportunity to locate input stream and buffer data operations more efficiently. Based on the above observation, we proposed a communication-traffic-assisted ADAS BOF vulnerability mining and exploitation method. Our method includes firmware extraction, a firmware and system analysis, the locating of risk points with communication traffic, validation, and exploitation. To demonstrate the effectiveness of our proposed method, we applied our method to several commercial ADAS devices and successfully mined BOF vulnerabilities. By exploiting these vulnerabilities, we executed the corresponding commands and mapped the attack to the physical world, showing the severity of these vulnerabilities.
Journal Article
Design of high-performance message middleware based on netty
As the number of sensors connected to the IoT cloud platform increases, the incoming data continues to increase, and the server may crash due to excessive traffic. In order to solve this problem and ensure the accuracy of the data received by the server, this paper designs a message middleware based on Netty. Message middleware is widely used in many systems, using message queues to achieve asynchronous communication and traffic peak reduction, and solve the problem of high data concurrency. Compare the mainstream messaging middlewares, select ActiveMQ middleware, design the consumer model according to the characteristics of the protocol, and perform idempotence processing on the consumer side to improve the reliability and security of the middleware.
Journal Article
A simple contagion process describes spreading of traffic jams in urban networks
The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two macroscopic characteristics for network traffic dynamics, namely congestion propagation rate
β
and congestion dissipation rate
μ
. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.
Predicting and controlling traffic congestion propagation is an ongoing challenge in most urban settings. Here, Seberi et al. apply a contagion model describing epidemic spread in population to model traffic jams, and verify its validity using large-scale data from six different cities around the world.
Journal Article
Fine-granularity inference and estimations to network traffic for SDN
An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.
Journal Article
Power communication digital flow prediction method based on VMD-LSTM-SVM model
Under the current trend of abundant information on power business, large data concentration, and large flow explosion, aiming at the randomness, volatility, and uncertainty of massive flow of electric power communication network, a digital power flow prediction method based on VMD-LSTM-SVM model is proposed. The interaction between the values of each traffic index before and after time is considered. LSTM is used to process traffic data and make an accurate prediction of future traffic. The power communication network can make dispatch responses to possible communication congestion by using link resources according to traffic prediction results and ensuring the transmission quality of power service data.
Journal Article
Delay Normalization Technique to Disrupt Covert Timing Channels Using Active Warden
Covert channels exploit existing network resources, such as packet headers and timing information, to transfer information in ways not originally intended for communication, making them undetectable by conventional methods. This poses significant security risks, as these channels can be used maliciously despite stringent security measures like firewalls. Hence, there is a critical need for a generalized mechanism capable of blindly detecting covert communications within network traffic. We have developed a framework to evaluate covert timing channels and conducted experiments with an active warden. Our findings indicate that the active warden effectively prevents covert timing communication, demonstrating the framework's potential for enhancing network security.
Journal Article