Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Proactive traffic responsive control based on state-space neural network and extended Kalman filter

The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Distributed Optimal Control for Traffic Networks with Fog Computing

This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.

Chaotic Control of Network Traffic

This paper presents a chaotic control method on network traffic. By this method, the chaotic network traffic can be controlled to pre-assigned equifibrium point according to chaotic prediction and the Largest Lyapunov Exponent （LLE） of the traffic on congested link is reduced, thereby the probability of traffic burst and network congestion can be reduced. Numerical examples show that this method is effective.

Networked control and supervision system based on LonWorks fieldbus and Intranet/Internet

A networked control and supervision system （NCSS） based on LonWorks fieldbus and lntranet/Intemet was designed, which was composed of the universal intelligent control nodes （ICNs）, the visual control and supervision configuration platforms （VCCP and VSCP） and an Intranet/Internet-based remote supervision platform （RSP）. The ICNs were connected to field devices, such as sensors, actuators and controllers. The VCCP and VSCP were implemented by means of a graphical programming environment and network management so as to simplify the tasks of programming and maintaining the ICNs. The RSP was employed to perform the remote supervision function, which was based on a three-layer browser/server（B/S） structure mode. The validity of the NCSS was demonstrated by laboratory experiments.

Traffic jam in signalized road network

Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS(speed of virtual signal) and VOS(velocity of spillover),were proposed respectively.SVS described the propagation of queue within a link while VOS reflected the spillover velocity of vehicle queue.Based on the two indexes,network jam simulation was carried out on a regular signalized road network.The simulation results show that:1) The propagation of traffic congestion on a signalized road network can be classified into two stages:virtual split driven stage and flow rate driven stage.The former stage is characterized by decreasing virtual split while the latter only depends on flow rate; 2) The jam propagation rate and direction are dependent on traffic demand distribution and other network parameters.The direction with higher demand gets more chance to be jammed.Our findings can serve as the basis of the prevention of the formation and propagation of network traffic jam.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Performance and robustness analysis of a fuzzy-immune flow controller in ATM networks with time-varying multiple time-delays

For the Asynchronous Transfer Mode （ATM） networks with time-varying multiple time-delays, a more realistic model for the available bit rate （ABR） traffic class with explicit rate feedback is introduced. A fuzzy-immune controller is designed, which can adjust the rates of ABR on-line, overcome the bad effect caused by the saturation nonlinearity and satisfy the weighted fairness. Also, the sufficient condition that guarantees the stability of the closed-loop system with a fuzzy-immune controller is presented in theory for the first time. The algorithm exhibits good performance, and most importantly, has a solid theoretical foundation and can be implemented in practice easily. Simulation results show that the control system is rapid, adaptive, robust, and meanwhile, the quality of service （QoS） is guaranteed.

PERFORMANCE ANALYSIS OF LEAKY BUCKET ALGORITHM WITH BURSTY TRAFFIC INPUT IN ATM NETWORKS

A bursty traffic model is introduced in this paper to describe the statistical characteristics of packet video. The performance of leady bucket algorithm with bursty traffic input is analyzed. The influences of various parameters on QOS (Quality of Service) are investigated. The analysis shows that although the loss probability decreases through expanding the buffer capacity, the delay and delay jitter increase, whose effect on QOS will not be negligible.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts.They are not aimed at the original network data,nor can they capture the potential characteristics of network packets.Therefore,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network traffic.Instead of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater depth.It regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted feature.Experimental results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.

Automated Routing Control System of Local Area Network Segment

Automated Routing Control System supersedes the prior approach to LAN redundancy which provides two or more LANs and each has a network (LAN) controller coupled to data communication devices. Devices require some software to switch between the network (LAN) controllers to counter some network segment failures. This approach is proven to be very costly due to demands for “off-the-shelf” data communication devices with built-in LAN controller drivers [1]. Automated Routing Control System, is the ultimate solution to the growing demands for inexpensive (less costly) and less difficult approach which requires less modification rather than upgrade of network devices yet it minimizes data and information exchange losses and interruptions caused by connection failures within the network and lessens the task of managing a complex network having many segments rather than subnets under a centralized monitoring and management. Automated Routing Control System rather than mechanism is a realistic approach applied to meet the ever growing demands for reliability, high efficiency and availability within data and information exchange networks.

APPLICATION OF INTELLIGENCE FORECASTING METHOD IN TRAFFIC ANALYSIS OF EGCS

Traffic flow forecasting is an important part of elevator group control system (EGCS).This paper applies time series prediction theories based on neural networks(NN) to EGCSs traffic analysis,and establishes a time series NN traffic flow forecasting model.Simulation results show its validity.

Analysis of Completion of Call with on-off Data Source in Wireless Networks

In wireless network, call completion probability accounts for users' satisfaction since the admitted ongoing call may be interrupted during hand-off process or even stay in the same cell when dynamically allocating resource to calls because of the loss of resource. We focus on the relationship between call's completion probability and these interruptions and develop an analytical relationship model for homogeneous cellular networks based on probability analysis. Then assuming call's data source is modeled by on-off traffic model, a two dimensional Markov process is established to compute these blocking and dropping probabilities for call's completion probability. The impacts of different new call arrival rate, call's traffic characteristic, user's mobility, call's holding time and call's admission threshold on call's completion are evaluated and compared through numerical examples. These results show that call's completion reaches its maximum value if making no difference between hand-off call and new call in the case of light traffic load. But some resource should be reserved for the hand-off call in high traffic scenario. The analytical model provides a basis for helping to set the call admission threshold. Key words on-off traffic source - call completion probability - call admission control - wireless networks CLC number TN 919. 72 Foundation item: Supported by the National Natural Science Foundation of China (60172077)Biography: XUAN Xiao-ying (1972-), female, Ph. D candidate, research direction: radio resource management and scheduling in wireless networks.

Class of Service Support Layer for Wireless Mesh Networks

This paper presents an add-on Class of Service (CoS) layer for wireless mesh networks. The proposed protocol is applicable for contention-based MACs and is therefore compatible with most of the Wireless Local Area Network (WLAN) and Wireless Sensor Network (WSN) protocols. The protocol has a locally centralized control for managing data flows, which either reserve a fixed bandwidth or are weighted by fair scheduling. The protocol reduces transmission collisions, thus improving the overall throughput. IEEE 802.11 adhoc WLAN has been taken as a platform for simulations and prototyping for evaluating the protocol performance. Network Simulator Version 2 (NS2) simulations show that the CoS protocol efficiently differentiates bandwidth, supports bandwidth reservations, and reaches less than 10 ms transfer delay on IEEE 802.11b WLAN. Testing with a full prototype implementation verified the performance of the protocol.

Wireless Hybrid QoS Architecture with an Enhancement of Fair Intelligent Congestion Control

More subtle and explicit QoS control mechanisms are required at the radio access level, even though the simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. At the radio access level, available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions;so streams of those session-based applications are admitted, established according to the traffic profile they require, and guaranteed. As the core in the Admission Control of the hybrid QoS architecture, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core via mechanisms of packet scheduling, buffer management, feedback and adjustments. It manages effectively the overloading scenario by preventing traffic violation from uncontrolled traffic, and providing guarantee to the priority traffic in terms of guaranteed bandwidth allocation and specified delay.

On Coexistence of Vehicular Overlay Network and H2H Terminals on PRACH in LTE

Vehicular ad hoc networks （VANETs） that use the IEEE 802.11p communication standard face a number of challenges, not least when it comes to safety messages on the VANET control channel （CCH） where short delay times and reliable delivery are of pri- mary importance. In this paper we propose a vehicular machine-to-machine （VM2M） overlay network that uses Long Term Evolu- tion （LTE） physical random access channel （PRACH） to emulate VANET CCH. The overlay network uses dedicated preambles to separate vehicular traffic from regular LTE traffic and a cartier sense multiple access with collision avoidance （CSMA-CA） layer similar to the one used in IEEE 802.15.4 to avoid the four step handshake and the overhead it incurs. The performance of the pro- posed overlay is evaluated under a wide range of PRACH parameters which conform to the scenarios with high vehicle velocities and large distances between roadside units （RSUs） that may be encountered in rural areas and on highways.

面向高性能计算的互连网络拥塞控制分析与评估

随着高性能计算技术的发展,高性能计算系统的网络节点数量不断增长,高性能计算应用对网络性能的要求越来越高,高性能互连网络的拥塞控制面临很大的压力与挑战。针对高性能计算互连网络的特点,研究高效、低开销的拥塞控制方法,是确保高性能互连网络性能和稳定性的关键。针对高性能计算系统中互连通信的核心问题,对主流的拥塞控制方法进行分析和实验比较;基于高性能计算系统的结构特点和通信特性,设计用于大规模模拟仿真的数据流模型和流文件生成工具,并提出一种拥塞控制综合评价指标;使用所提出的数据流模型,在较大规模网络中对不同拥塞控制方法进行模拟,并基于所提出的评价指标对几种拥塞控制方法的性能进行分析和评估。提出的分析和评估技术可以对高性能互连网络的拥塞控制方法进行更客观和准确的分析与评估。