Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

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.

An improved BP artificial neural network algorithm for urban traffic flow intelligent prediction

The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.

On Minimizing Delay with Probabilistic Splitting of Traffic Flow in Heterogeneous Wireless Networks

In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.

Design of Expressway Toll Station Based on Neural Network and Traffic Flow

This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow. Firstly, the design of the toll plaza is mainly through analyzing the daily traffic flow, different charging mode of construction cost and waiting time of the United States. Secondly, exploring traffic conditions is divided into two kinds, based on the traffic flow speed-density flow model. Then, a fuzzy-BP neural network model is constructed, with capacity, cost, and safety factor as the input layers and performance as the output layer. It is concluded that this scheme will reduce the occurrence of traffic accidents, so it is desirable. Considering that the increase in unmanned vehicles will lead to an increase in safety performance, we increase the number of electronic toll stations to improve security performance and reduce the occurrence of traffic accidents.

Prediction of elevator traffic flow based on SVM and phase space reconstruction

To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.

A modification of local path marginal cost on the dynamic traffic network

Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local PMC,considering marginal cost of partial links, is normallycalculated to approximate the global PMC. When analyzingthe marginal cost at a congested diverge intersection, ajump-point phenomenon may occur. It manifests as alikelihood that a vehicle may unsteadily lift up （down） inthe cumulative flow curve of the downstream links. Previously,the jump-point caused delay was ignored whencalculating the local PMC. This article proposes an analyticalmethod to solve this delay which can contribute toobtaining a more accurate local PMC. Next to that, we usea simple case to calculate the previously local PMC and themodified one. The test shows a large gap between them,which means that this delay should not be omitted in thelocal PMC calculation.

Novel Real-Time System for Traffic Flow Classification and Prediction

Traffic flow prediction has been applied into many wireless communication applications(e.g., smart city, Internet of Things). With the development of wireless communication technologies and artificial intelligence, how to design a system for real-time traffic flow prediction and receive high accuracy of prediction are urgent problems for both researchers and equipment suppliers. This paper presents a novel real-time system for traffic flow prediction. Different from the single algorithm for traffic flow prediction, our novel system firstly utilizes dynamic time wrapping to judge whether traffic flow data has regularity,realizing traffic flow data classification. After traffic flow data classification, we respectively make use of XGBoost and wavelet transform-echo state network to predict traffic flow data according to their regularity. Moreover, in order to realize real-time classification and prediction, we apply Spark/Hadoop computing platform to process large amounts of traffic data. Numerical results show that the proposed novel system has better performance and higher accuracy than other schemes.

Behaviours in a dynamical model of traffic assignment with elastic demand

This paper investigates the dynamical behaviour of network traffic flow. Assume that trip rates may be influenced by the level of service on the network and travellers are willing to take a faster route. A discrete dynamical model for the day-to-day adjustment process of route choice is presented. The model is then applied to a simple network for analysing the day-to-day behaviours of network flow. It finds that equilibrium is arrived if network flow consists of travellers not very sensitive to the differences of travel cost. Oscillations and chaos of network traffic flow are also found when travellers are sensitive to the travel cost and travel demand in a simple network.

智能网联环境下混合交通流仿真设计与特性分析

以智能网联环境下混合交通流的特性为研究对象,在SUMO仿真软件中设置高速公路双向六车道的交通环境,利用SUMO仿真软件和Python软件对模拟智能网联汽车的CACC(cooperative adaptive cruise control)模型和模拟人工驾驶汽车的IDM(intelligent driver model)模型进行仿真实验,并对CACC汽车不同渗透率下的数据进行对比分析,从而对智能网联汽车与人工驾驶汽车形成的混合交通流模型进行研究。模拟仿真结果表明:在不同渗透率的智能网联汽车和人工驾驶汽车的混合交通流中,随着CACC汽车渗透率的不断增加,道路的流率不断提高,平均速度也不断提高;当CACC汽车渗透率达到100%时,道路的总时间延误可以减少约862 s;随着CACC汽车渗透率的不断增加,道路上车辆的换道频率也随之下降,减少了道路上车辆之间的速度差。智能网联汽车的参与可以提高高速公路的通行能力和车道的利用率,提高车辆的平均速度并减少行程的时间延误。

基于Echo State Neural Networks的短期交通流预测算法

在城市交通环境,交通流的正确预测是比较困难,因为多个十字路口,这使得预置的交通控制模型之间的相互作用和intertwinement不能保持始终高性能在所有的交通情况。

基于TensorFlow的交通标志识别方法研究

交通标志识别系统是智能驾驶系统的重要组成部分;本文分析了现有方法存在的问题,基于TensorFlow框架搭建了改进的卷积神经网络,用于识别交通标志;整个系统在TensorFlow上实现,使用行车记录仪采集的视频验证了本文的算法,结果表明本文算法有一定的实用性,而且在准确率,鲁棒性和实时性等方面也表现较好。

Modeling and Generating Realistic Background Traffic by Hybrid Approach

One of the key challenges in largescale network simulation is the huge computation demand in fine-grained traffic simulation.Apart from using high-performance computing facilities and parallelism techniques,an alternative is to replace the background traffic by simplified abstract models such as fluid flows.This paper suggests a hybrid modeling approach for background traffic,which combines ON/OFF model with TCP activities.The ON/OFF model is to characterize the application activities,and the ordinary differential equations(ODEs) based on fluid flows is to describe the TCP congestion avoidance functionality.The apparent merits of this approach are(1) to accurately capture the traffic self-similarity at source level,(2) properly reflect the network dynamics,and(3) efficiently decrease the computational complexity.The experimental results show that the approach perfectly makes a proper trade-off between accuracy and complexity in background traffic simulation.

基于深度学习的Android恶意软件动态检测

为提高Android恶意软件的检测精度,提出一种基于改进DenseNet网络的Android恶意软件动态检测方法。以应用软件运行特定阶段的网络通信流量为分析对象,根据会话五元组信息切分原始网络流量并转换为灰度图,提出一种基于DenseNet网络改进的分类检测网络DenseNet_IS。通过添加具有不同大小卷积核的卷积分支获取不同感受野的特征,通过引入SimAM注意力模块,从空间和通道两个维度实现对重要特征的关注。结合应用软件判决机制,实现最终分类。在CICAndMal2017数据集上的实验结果表明,所提方法可以达到99.06%的良恶性检测精度和96.51%的多分类精度,验证了该方法的有效性。

考虑交通流量俘获的电动汽车充电负荷预测和充电站规划

针对电动汽车(EV)的充电需求,考虑路径的交通流量,以最大交通流量俘获、最小配电系统网络损耗和最小节点电压偏移为目标,构建了一个多目标决策模型对EV充电站进行规划。运用网络扩展技术确定交通流量俘获路径;运用蒙特卡罗模拟算法,确定规划区内EV的最大充电负荷,从而推算得到充电站的容量;运用超效率数据包络分析评价方法,确定经过归一化处理后各目标函数的权重系数,从而将多目标优化问题转化为单目标优化问题,并采用改进的二进制粒子群优化算法进行求解。以一个包含25个节点的交通网络耦合33节点配电系统为算例进行仿真,验证所建模型和所提方法的有效性,并进一步分析EV最大行驶里程、充电站负荷接入不同节点以及不同时刻对各目标函数的影响。

基于复杂网络的空中交通流量短期预测

为合理预测空中交通流量,结合复杂网络链路预测进行研究。首先,将时间序列转化为可视图得到拓扑特征量,然后结合基于局部信息、路径和随机游走的算法,比较在三亚不同扇区内的预测精度,发现RWR0.85算法预测精度最高。由于链路预测只能预测可能存在的连边,不能预测节点,因此引入D⁃S证据理论预测流量值,预测精度最高可达99.85%。结果表明,复杂网络链路预测结合D⁃S证据理论进行空中交通流量的预测是可行有效的,为进一步深入研究奠定了基础。

基于改进NaSch模型的网联异质交通流特性分析

为研究智能网联环境下异质交通流演变规律,设计典型场景驾驶模拟实验,采集传统车辆(human driven vehicle,HDV)与智能网联车辆(connected vehicle,CV)驾驶行为特征指标,对异质交通流关键参数进行分析和标定;构建考虑HDV与CV驾驶行为差异的异质交通流元胞自动机模型;并基于改进的NaSch模型进行仿真实验,解析智能网联环境下交通流基本图,分析异质交通流特性。研究结果表明:较于HDV,CV驾驶员捕捉道路信息和反应时间提升约11.4%;自由流状态下,CV车速比HDV车速提升了7.4%,且同一车速下安全跟驰距离缩短了18.2%;随着CV所占比例由20%增至80%,交通流基本图显示交通流平均车速显著提升,交通流率增加,时空轨迹图显示局部拥堵状况明显改善。

基于GWO-HMM的空中交通网络流系统态势预测研究

针对空中交通流量管理部门如何更高效地实施流量管理的问题,本文将态势感知理论应用于空中交通网络流系统(ATNFS,air traffic network flow system),建立空中交通网络流系统的运行态势预测模型。首先,给出了空中交通网络流系统的态势感知过程,从节点和航线的角度筛选出航线饱和度、不正常航班率、节点饱和度、节点延误架次比、节点航班取消率5个态势要素,使用态势值作为态势理解的指标;其次,分析隐马尔可夫模型(HMM,hidden Markov model)的优势与不足,建立了基于灰狼优化(GWO,grey wolf optimization)算法和改进隐马尔可夫模型的态势预测模型;最后,使用某空中交通网络流系统的实际运行数据进行算例验证。结果表明,改进后的预测模型相较于原本的隐马尔可夫预测模型精度更高,预测结果更准确。

基于循环独立机制的交通流量预测

交通流量预测是智能交通控制和管理系统的一个重要环节,但交通流量数据具有时间和空间上的非线性和复杂性等特征,为对其进行精准预测,本文提出了Graph Temopral Recurrent Independent Mechanisms (G-tRIM)模型。该模型使用图注意力网络(Graph Attention Networks, GAT)来有效捕获交通流量数据的空间依赖关系,使用循环独立机制(Recurrent Independent Mechanisms, RIM)来精准刻画交通流量数据的潜在状态。最后在北京和贵州数据集上,以均方误差(Mean Square Error, MSE)和平均绝对误差(Mean Absolute Error, MAE)为指标进行实验,结果表明,G-tRIM在各个数据集上的表现均优于基准模型。