Network Traffic Flow Research Articles

Assessing the effects of link-repair sequences on road network resilience

Disruptions to transport networks are inevitable and detrimental to the functioning of society. Improving the resilience of transport networks to disruptive events has, therefore, a significant impact on society. Although the resilience of a transport system depends on the ability of the network to sustain the consequences of initial disruption (i.e. robustness) and quickly recover its performance (i.e. rapidity), the latter attracted less attention than robustness. The present paper focuses on quantifying the impacts of recovery processes and, more specifically, link-repair strategies on resilience. Several link-repair strategies are compared across a multitude of perturbation scenarios in the well-known Sioux Falls network. The strategies considered include: (i) the optimal (minimising the disruption consequences over the recovery process), (ii) average (representing a recovery process where the disrupted links are repaired in random order), (iii) flow-based (where the links with the highest traffic flow in the undisrupted network are repaired first), and (iv) criticality-based (where the links whose individual failure result in the highest impacts on the system performance are repaired first) recovery. The results of this comparison are subsequently used to evaluate the correlation between robustness and resilience, and characterise the optimal repair strategy.

Information Theory-based Approaches to Detect DDoS Attacks on Software-defined Networking Controller a Review

The number of network users and devices has exponentially increased in the last few decades, giving rise to sophisticated security threats while processing users’ and devices’ network data. Software-Defined Networking (SDN) introduces many new features, but none is more revolutionary than separating the control plane from the data plane. The separation helps DDoS attack detection mechanisms by introducing novel features and functionalities. Since the controller is the most critical part of the SDN network, its ability to control and monitor network traffic flow behavior ensures the network functions properly and smoothly. However, the controller’s importance to the SDN network makes it an attractive target for attackers. Distributed Denial of Service (DDoS) attack is one of the major threats to network security. This paper presents a comprehensive review of information theory-based approaches to detect low-rate and high-rate DDoS attacks on SDN controllers. Additionally, this paper provides a qualitative comparison between this work and the existing reviews on DDoS attack detection approaches using various metrics to highlight this work’s uniqueness. Moreover, this paper provides in-depth discussion and insight into the existing DDoS attack detection approaches to point out their weaknesses that open the avenue for future research directions. Meanwhile, the finding of this paper can be used by other researchers to propose a new or enhanced approach to protect SDN controllers from the threats of DDoS attacks by accurately detecting both low-rate and high-rate DDoS attacks.

Visualization of Anomalies using Graph-Based Anomaly Detection

Network protocol analyzers such asWireshark are valuable for analyzing network traffic but pose a challenge in that it can be difficult to determine which behaviors are out of the ordinary due to the volume of data that must be analyzed. Network anomaly detection systems can provide vital insights to security analysts to supplement protocol analyzers, but this feedback can be difficult to interpret due to the complexity of the algorithms used and the lack of context to determine the reasoning for which an event was labeled as anomalous. We present an approach for visualizing anomalies using a graph-based anomaly detection methodology that aims to provide visual context to network traffic. We demonstrate the approach using network traffic flows as an approach for aiding in the investigation and triage of anomalous network events. The simplicity of a visual representation supports fast analysis of anomalous traffic to identify true positives from false positives and prevent further potential damage.

Spark Cloud-Based Parallel Computing for Traffic Network Flow Predictive Control Using Non-Analytical Predictive Model

When dealing with traffic big data under the background of Internet of Things (IoT), traffic control under the single-machine computing environment is difficult to adapt to the massive and rapid analysis and decision-making. To tackle this problem, we propose a parallel computing approach of traffic network flow control based on the mechanism of model predictive control (MPC). A non-analytical rule-based traffic flow model is developed to forecast vehicle movements in the prediction horizon according to the real-time feedback information of traffic flow, and evaluate the performances of candidate control strategies. Furthermore, to accelerate the solution process of obtaining the optimal control schemes in the prediction horizon, a two-level hierarchical parallel genetic algorithm (HPGA) based on Spark cloud computing is designed. Through the parallel computing architecture, the computationally intensive optimization tasks are decomposed into multiple parallel sub-tasks with the aid of resilient distributed datasets (RDDs), which improves the computational efficiency. The simulation results demonstrate the validity of the proposed methodology for traffic network flow predictive control with respect to unsaturated and oversaturated traffic scenarios. The Spark-based parallel optimization approach has the potential to satisfy the computing requirements of online optimization when dealing with the big data of traffic network flow control while keeps favorable control performances.

Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors

The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city.

Teaching Management and Monitoring Abnormal Network Behaviors Under COVID-19

Due to the epidemic of COVID-19, more social activities have been moved to the internet, such as online education and online learning. The education management to avoid burst events is a basic requirement of online education, especially when a huge number of persons are visiting at the same time. In order to monitor the abnormal and burst access in online education systems, this paper proposes an anomaly detection method by using data flow to mining high frequency events among massive network traffic data during online education. First, the data flow in traffic network is described as a special structure which is used to establish an efficient high frequent event detection algorithm. Second, the network traffic flow is reduced to make it possible to monitor large-scale concurrent network visiting. The effectiveness of the abnormal network behavior detection method is verified through the experiment on a real network environment for online education.

Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection

The explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forward a large amount of traffic to damage one or all target’s resources simultaneously and prevents authenticated users from accessing network services. The paper aims to select the least number of relevant DDoS attack detection features by designing an intelligent wrapper feature selection model that utilizes a binary-particle swarm optimization algorithm with a decision tree classifier. In this paper, the Binary-particle swarm optimization algorithm is used to resolve discrete optimization problems such as feature selection and decision tree classifier as a performance evaluator to evaluate the wrapper model’s accuracy using the selected features from the network traffic flows. The model’s intelligence is indicated by selecting 19 convenient features out of 76 features of the dataset. The experiments were accomplished on a large DDoS dataset. The optimal selected features were evaluated with different machine learning algorithms by performance measurement metrics regarding the accuracy, Recall, Precision, and F1-score to detect DDoS attacks. The proposed model showed a high accuracy rate by decision tree classifier 99.52%, random forest 96.94%, and multi-layer perceptron 90.06 %. Also, the paper compares the outcome of the proposed model with previous feature selection models in terms of performance measurement metrics. This outcome will be useful for improving DDoS attack detection systems based on machine learning algorithms. It is also probably applied to other research topics such as DDoS attack detection in the cloud environment and DDoS attack mitigation systems.

Route Choice Behavior Modeling for Emergency Evacuation and Efficiency Analysis Based on Type-II Fuzzy Theory

For route choice during an outside emergency evacuation, evacuees make different route choice decisions due to their preference diversity. Furthermore, uncertainties of human behavior are inevitable, leading to a high complexity of macroscopic traffic flows and great impact on the evacuation efficiency. In this work, a simulation-based evacuation traffic planning is presented for homogenizing the network traffic flows. In the simulation, the evacuees are divided into two types: panicky and rational ones, and their route choice behavior is captured by two distinct models, respectively. The proportion of panicky evacuees is dynamically changed based on the traffic information level (TIL) and traffic states of downstream links at a decision point. Type-II fuzzy logic system (T2FLS) is used to model the uncertainties of evacuees’ subjective perception on route costs. Comprehensive numerical experiments are done to analyze the impact of TIL, behavior diversity, and the heterogeneity of macroscopic traffic flows on evacuation efficiency. Results show that the proposed evacuation traffic planning is beneficial for improving the evacuation efficiency, alleviating the proportion of panicky evacuees, and homogenizing traffic flows in a large-scale network.

A Study on Various Technologies to Solve the Routing Problem in Internet of Vehicles (IoV)

Internet of Vehicles (IoV) can be pivotal factor towards realization of Intelligent Transportation Systems. IoV principle focus is to have time decisive safety applications, optimize traffic flow, infotainment and Vehicular network with the intention to improve road safety through deployment of application allowing drivers to anticipate danger on the road. One of the important challenges of IoV is timely, reliable, and consistent propagation of messages among vehicles which enable drivers to take appropriate decisions to have improved road safety. Many proposals has been put forward by researchers to identify the traffic jam and routing the vehicular nodes in urban and highway roads for consistent, safe and secured driving environment. Even though the protocols have several limitations including lack of scalability to larger networks, routing overheads, etc. To overcome these limitations bio-inspired, big data, genetic algorithm, machine learning approaches have been proposed to identify and route packets among vehicular nodes in an optimized manner. The paper contains the survey of already proposed method and new approach to identify and route the vehicular node for the IoV environment.

Efficient execution plan for egress traffic engineering

The main objective of Egress Peer Engineering (EPE) is to steer traffic exiting one Autonomous System (AS) to another in the most cost-effective way by assigning network traffic flows of different destinations to internal routes specific to every AS. The traffic assignment process is carried out to satisfy network operators’ objectives ,which include optimizing resource utilization, minimizing monetary costs and avoiding overloading the peer links. Due to network traffic dynamicity and unpredictability, traffic assignments should be constantly updated so that the aforementioned objectives are satisfied. Each of these updates results in traffic assignment changes that transition the network to a more optimized state. Executing these changes all at once is detrimental to the internal network infrastructure. To tackle this issue, this work targets finding execution plans involving several intermediate steps whereby each step includes a subset of traffic assignment changes. While executing these steps, the network operator’s objectives need to be guaranteed. To that end, an oracle algorithm that generates all the possible balanced subsets of traffic changes as execution plans is formulated. This algorithm is compared to two heuristics that are designed based on an analytical study of the problem itself. To effectively evaluate these algorithms, evaluation criteria are devised that encompass several technical and design quality metrics of the desired execution plan. These three approaches are evaluated on network configurations of small size networks, and the results obtained show that one of the heuristics outperforms the oracle implementation in terms of execution time while producing comparable results based on the evaluation criteria. For big networks, the best performing heuristic satisfies the quality metrics and generates its best execution plan in a short period of time.

A comprehensive importance assessment method of highspeed railway network: a case study in China

A comprehensive importance measure method of railway network components is proposed in this paper. First, the railway network construction method is developed based on the physical layout and travel time. The betweenness centrality and network efficiency are extended by combining the network topology and traffic flow to measure the link and node importance. Because single available measure cannot reflect the overall network component criticality, a comprehensive importance measure method is developed by using the weighted average value of the two metrics. Finally, the proposed method is applied to measure the link and node importance of Chinese high speed railway network. A complete picture of railway network component criticality is obtained.

Research on risk prevention and control strategy of power grid CPS system based on intrusion tolerance

In this paper, an information-physical risk prevention and control framework based on intrusion tolerance technology is proposed, which is a multi-stage in-depth defense system for the risk interaction mechanism and evolution process in the power information-physical fusion system, the aim of this paper is to improve the survivability of the system under attack and minimize the loss caused by the risk through multi-phase defense strategy. At the same time, according to the phase characteristics of risk propagation in CPS, a safe operation state transition model considering intrusion tolerance is established. In order to contain the critical failure events in the risk evolution process of the information-physics fusion system, according to the risk development characteristics of the information-physics system under attack, this paper presents a self-organized critical state identification method, which integrates the characteristics of physical network power flow and information network traffic flow, in order to identify the critical nides that make CPS system enter the self-organized critical state under network attack. Finally, according to the characteristics of CPS dependent network structure and the self-organized critical characteristics of risk evolution, the protection strategies of strengthening the protection of key nodes, adjusting the coupling state of dependent edges and adding autonomous nodes are formulated, by considering the decision-making model of risk prevention and control strategy in the game of attack and defense, the current defense resources are reasonably allocated.

Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

Mathematical programming formulations for robust airside terminal traffic flow optimisation problem

The robust traffic flow modelling approach offers a perspicacious and holistic surveillance for flight activities in a nearby terminal manoeuvring area. The real time flight information expedites the streaming control of terminal operations using computational intelligence. Hence, in order to reduce the adverse effect of severe uncertainty and the impact of delay propagation, the amplified disruption along with the terminal traffic flow network can be leveraged by using robust optimisation. The transit time from entry waypoint to actual landing time is uncertain since the true airspeed is affected by the wind direction and hazardous aviation weather in the terminal manoeuvring area. Robust optimisation for TTFP is to generate a solution against the uncertain outcomes, which implies that less effort by the ATC to perform re-scheduling is required. In addition, two decomposition methods are presented and proposed in this work. The computational performance of traditional Benders Decomposition will largely be affected by the infeasibility in the subsystem and resolution of infeasible solution in the second-stage optimisation problem resulting in a long iterative process. Therefore, we presented an enhanced Benders Decomposition method to tackle the infeasibility in the subsystem. As shown in the numerical experiments, the proposed method outperforms the traditional Benders Decomposition algorithm using Wilcoxon-signed ranks test and achieved a 58.52% improvement of solution quality in terms of solving one-hour flight traffic scenarios with an hour computation time limit.

A Link Queue Model of Network Traffic Flow

Fundamental to many transportation network studies, traffic flow models can be used to describe traffic dynamics determined by drivers’ car-following, lane-changing, merging, and diverging behaviors. In this study, we develop a deterministic queueing model of network traffic flow, in which traffic on each link is considered as a queue. In the link queue model (LQM), the demand and supply of a link queue are defined in the queue size (number of vehicles), and its in- and out-flows are computed from junction flux functions corresponding to macroscopic merging and diverging rules. The new model is a system of ordinary differential equations that is mathematically tractable and computationally efficient and can capture queue spillbacks and interactions among links. We further demonstrate that the LQM is fundamentally different from the cell transmission model (CTM) and link transmission model (LTM) for a road segment, a signalized ring road, and a diverge-merge network, with respect to the shock and rarefaction waves, network fundamental diagram, and stability property. In a sense, the new model is a space-continuous approximation of the kinematic wave model and can be a useful addition to the multiscale modeling framework of network traffic flow. The model has been applied to formulate and solve network traffic control and observation problems.

A Network Traffic Classification Method Based on Graph Convolution and LSTM

In the identification of normal and abnormal traffic flows, Convolutional Neural Network (CNN) is commonly used to extract spatial features of network traffic at present. However, its limitation is that the one-dimensional form of traffic flow data needs to be converted into two-dimensional form, without considering the potential spatial correlation between traffic flows. In view of the potential correlation between network traffic flows, this paper proposes a classification method based on graph convolution and Long-Short Term Memory (LSTM). First, perform data preprocessing on the traffic flow data, then use the graph convolutional network to extract the spatial features of spatial topology and use LSTM to extract its temporal features. Finally, the performance of the algorithm is evaluated on the sampled UNSW-NB15 data set. Experimental results show that the proposed method can effectively extract the potential features of network traffic data. Compared with other methods such as feature selection, bidirectional LSTM (BiDLSTM) and CNN-LSTM, it proves the effectiveness of the proposed algorithm and performs better in classification performance.

Model and algorithm of stochastic dynamic traffic assignment based on dynamic rainfall intensity

Frequent occurrence of urban rainy weather, especially rainstorm weather, affects transportation operation and safety, so it is essential that effective intervention measures to recover disordered traffic be adopted and then analyzed for their influence on the dynamic network. Therefore, models and algorithm to show dynamic traffic flow of traffic network in rainy weather are a fundamental need and have drawn great interest from governments and scholars. In this paper, innovative content contains a travel cost function considering rainfall intensity; considering the travel cost function, a dynamic traffic assignment model based on dynamic rainfall intensity is built. Then a corresponding algorithm is designed. Moreover, this study designs three scenarios under rainfall and analyzes the influence of the rainfall on an example network. The results show that rainfall has a significant effect on traffic flow. The finding proved the proposed models and algorithm can express the development trend of path flow rate on a dynamic network under rainfall.

Analysis of Traffic Flow Equilibrium in Urban Road Network Based on Floating Vehicle Data

Based on the floating vehicle data, this paper analyzes the equilibrium of urban road network traffic flow. This can guide traffic flow distribution and provide reliable basis for traffic control. This paper starts with the quantitative analysis of the traffic network equilibrium, on the basis of verifying the validity of the floating car data, divides the urban road network into regions, and constructs an analysis model of the traffic flow equilibrium of the urban road network. The urban road traffic distribution model is constructed in accordance with the number of road traffic segments. On this basis, gini coefficient index is introduced to judge the road network flow balance, which is used to analyze the balance of each sub-region. By means of traffic guidance, signal control and other traffic control means, the traffic flow in each sub-area is balanced, and the traffic flow in the whole road network becomes balanced.

СУЧАСНА ІНФРАСТРУКТУРА КОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ У БІЗНЕС-СЕРЕДОВИЩІ: МОДЕЛЮВАННЯ МЕТОДУ «ЖИВОЇ МІГРАЦІЇ»

In order to meet data and service requirements, network operators are constantly expanding and improving their network infrastructure, resulting in increased capital and operating costs. However, due to intense competition and falling prices, the average income per user does not increase proportionally, which leads to a decrease in return on investment. Thus, to reduce costs and increase revenue, mobile networks need to make the next evolutionary leap towards 5G, which now applies not only to the mobile border, but also to the core network. The 5G micro-operator service architecture must also be developed together with various technologies such as SDN and NFV. SDN abstracts network architecture by separating network management and redirection functions, allowing network management to become directly programmable and the underlying infrastructure to be abstracted for applications and network services. The interaction between SDN and NFV allows the 5G network to abstractly build the system infrastructure and further increase network flexibility. Therefore, the article substantiates the possibility of market commercialization of the use of technologies of live migration of traffic flow, which allows to give the product unique characteristics. The existing approaches to the solution of the problem of load balancing of the network of 5G micro operators are analyzed. A number of advantages of the live migration method have been identified, namely the possibility of its application for the micro operator's network and efficient use of network resources. According to the results of the experiment, it was found that the method of live migration has better values µO_num (68.1% of traffic flow) than the mechanism of MLF (29.8% of traffic flow). It is proved that the mechanism of live migration can determine the priority of the user traffic flow according to the servers in the zone µO, and therefore it is expedient to apply in terms of optimizing the distribution of traffic flow. The proposed method should increase the utilization of network resources and traffic flow efficiency and lead to a higher level of experience quality (QoE) for network users.