Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Identification and Prediction of Internet Traffic Using Artificial Neural Networks

This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.

Studying cost-sensitive learning for multi-class imbalance in Internet traffic classification

Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results. But the classification performance on the minority classes with a few bytes is still unhopeful because the existing research only focuses on the classes with a large amount of bytes. Therefore, the class-dependent misclassification cost is studied. Firstly, the flow rate based cost matrix （FCM） is investigated. Secondly, a new cost matrix named weighted cost matrix （WCM） is proposed, which calculates a reasonable weight for each cost of FCM by regarding the data imbalance degree and classification accuracy of each class. It is able to further improve the classification performance on the difficult minority class （the class with more flows but worse classification accuracy）. Experimental results on twelve real traffic datasets show that FCM and WCM obtain more than 92% flow g-mean and 80% byte g-mean on average; on the test set collected one year later, WCM outperforms FCM in terms of stability.

Detecting and Classifying Darknet Traffic Using Deep Network Chains

The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime.Furthermore,classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur.This paper presents a two-stage deep network chain for detecting and classifying darknet traffic.In the first stage,anonymized darknet traffic,including VPN and Tor traffic related to hidden services provided by darknets,is detected.In the second stage,traffic related to VPNs and Tor services is classified based on their respective applications.The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic.It achieved an accuracy of 96.8%and 94.4%in the detection and classification stages,respectively.Optimization and parameter tuning were performed in both stages to achieve more accurate results,enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks.In the classification stage,it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols.However,in cases where it is desired to distinguish between such activities accurately,the presented deep chain classifier can accommodate additional classifiers.Furthermore,additional classifiers could be added to the chain to categorize specific activities of interest further.

城市网红经济的形成与传导机制--兼论淄博烧烤走红的区域经济学意义

淄博烧烤由网络热点下的区域性经济事件发展成为区域公用品牌,有效带动了周边区域经济的发展。在促进区域经济增长、推动产业转型升级、提升城市品牌建设和优化空间资源配置等方面具有重大经济意义。以区域经济学视角为切入点,基于淄博烧烤事件发展过程和特征,深入剖析淄博烧烤走红对以淄博为核心的区域经济的影响,结合现实经验和未来挑战梳理淄博烧烤事件的区域经济意义。研究发现,鼓励民营经济发展、提升城市服务水平、发掘区域文化资源、抢抓流量营销机遇、扩大区域消费市场和推动政府-市场协同用力是淄博烧烤走红的六大核心机制;保持服务品质、应对市场竞争、提升创新能力是淄博烧烤行业长期发展中的关键着力点。淄博烧烤的成功为有效应对流量经济潮流,打造新型消费热点,促进区域经济转型提供有益参考,在我国推动有为政府和有效市场高效结合,激发人口大市场消费动能,促进区域经济一体化和高质量发展中具有重要实践意义。

基于物联网的交通信号灯控制系统

随着城市化进程的不断加速,交通拥堵问题成为制约城市可持续发展的一大挑战。在智能交通系统中,交通信号灯作为关键组成部分,对道路交通的管理和优化发挥着至关重要的作用。为提高交通效率、减少交通拥堵,文章探讨了基于物联网的智能交通信号灯控制系统。首先,介绍了交通信号灯的系统组成。其次,针对交通信号灯控制的优化需求,提出一种基于物联网的自适应控制算法。最后,构建了交通信号灯仿真界面,并设计了交互控制按钮。实验结果表明,所提出的自适应控制算法能够有效提升道路通行效率,降低交通拥堵程度,为智能交通系统的进一步优化与发展提供了重要参考。

基于模糊逻辑的物联网流量攻击检测技术综述

物联网越来越多地出现在日常活动中,将我们周围多样化的物理设备连接到互联网,奠定了智慧城市、电子健康、精准农业等应用的基础。随着物联网应用的迅速普及,针对这类设备和服务的网络攻击数量也有所增加,且这些攻击具有不精确性和不确定性,使得对其进行正确检测和识别更加困难。为了应对上述挑战,学者们引入了基于模糊逻辑的攻击检测框架,在各种操作步骤中结合不同的模糊技术,以便在数据不准确和不确定时更精确地检测网络攻击。文中首先对物联网的安全性进行了详细的探讨,如其应对的安全挑战、所需的安全要求、面临的攻击类型等;其次对入侵检测系统(Intrusion Detection Systems,IDS)进行了描述,进而简述了物联网中IDS的基础框架;然后阐述了模糊逻辑的技术原理,分析了将其应用在流量攻击检测中的合理性;接着比较了各种基于不同技术的流量攻击检测方案,以说明它们在该领域的性能和重要性;最后总结了本文的主要工作,指出了未来的研究方向,为该领域的研究者提供了新的视角,以更好地应对不断升级的网络攻击。

Internet流量模型的研究

对于网络性能评价,一种有效的流量模型是不可或缺的,然而很难找到这种既简单又正确的模型。本文吸取了经验模型的TCP连接流量模型的优点,提出一种WWW流量模型。通过严格的仿真和模型验证,证明该模型与实际的流量吻合得很好,且易于实现。

基于物联网的隧道通风控制系统的研究与设计

针对隧道通风采用的分档控制与模糊控制的各自缺点,提出一种基于LSTM交通流预测的模糊PID智能算法。利用LSTM算法预测未来的污染物趋势,按照计算出的需风量提前开启风机,改善系统的滞后性,并利用模糊PID算法对风机进行精确控制。引入物联网技术,构建“互联网+隧道通风”的控制系统,利用PLC网关将采集的数据上传至云平台,供运营商随时随地查看隧道各节点的数据并对设备进行远程调控。仿真实验数据表明,算法超调量为4%,调节时间较模糊控制缩短68.60%,并具有较强的鲁棒性。

基于物联网的城市交叉路段交通信号智能控制方法

城市交叉路段交通信号采集系统采集到的信号频谱中线谱信号较少,导致采集速度较慢。为了解决上述问题,提出基于物联网的城市交叉路段交通信号智能控制方法。系统硬件选用FL2440微处理器处理数据,通过YHSG-86G的传感器放大信号。利用双通道模式输出通信数字,选择具备三路HDMI信号与一路HDMI信号自动切换功能的嵌入器。采用GH89S信号采集器提高系统对交通信号的采集效率。在硬件设计的基础上,引入模糊关联规则算法对交通信号数据进行挖掘,通过数据分类、挖掘交叉路段交通信号数据、信号标识、信号存储获取交通信号数据。以此实现城市交叉路段交通信号智能控制。实验结果表明,所提方法能够更清晰地显示出信号频谱中的线谱信号,提高信号的采集速度。

新增未知攻击场景下的工业互联网恶意流量识别方法

针对工业互联网中新增未知攻击所引发的流量数据分布偏移问题,提出了一种基于邻域过滤和稳定学习的恶意流量识别方法,旨在增强现有图神经网络模型在识别已知类恶意流量时的有效性和鲁棒性。该方法首先对流量数据进行图结构建模,捕获通信行为中的拓扑关系与交互模式;然后,基于有偏采样的邻域过滤机制划分流量子图,消除通信行为间的伪同质性;最后,应用图表示学习和稳定学习策略,结合自适应样本加权与协同损失优化方法,实现高维流量特征的统计独立性。2个基准数据集上的实验结果表明,相较对比方法,所提方法在新增未知攻击场景下的识别性能提升超过2.7%,展示了其在工业互联网环境下的高效性和实用性。

MPLS和Internet流量工程

Rapid growth of Internet traffic and increasing requirement for service quality have made traffic engineering an essential consideration in the operation of large Internet backbone networks. Historically,traffic engineering in the Internet has been hampered by the conventional IP technologies. This article discusses the challenge of Internet Traffic Engineering and the applications of MPLS to traffic engineering in IP networks.

流量经济下的互联网产业劳动组织模式研究——基于S市D公司

相较于工业时代,互联网时代的劳动组织模式已经发生了变化。通过对某互联网公司的田野调查和员工访谈,本文发现企业由于流量经济的影响,陷入了劳动力频繁流动与劳动力培养成本之间的两难。为解决该问题,使劳动力的使用效率达到最大化,企业首先会严格地筛选符合其需求的劳动者,并在生产领域和再生产领域满足劳动者的自主性,笔者将其概括为雇员投资型体制。在这一体制下,劳动力不再被视为企业应控制的成本,而转变为可供企业投资的对象。这种肯定性的劳动组织模式对传统否定性的劳动组织模式的取代,解决了互联网产业中的劳动力使用问题。然而,这也将企业面临的挑战转嫁于劳动者,增加了劳动者面临的工作压力,使他们陷入自主性越强、工作强度越大的悖论之中。

Internet中的相变现象及一种新的相变业务流模型

本文从宏观的角度阐述了Internet的网络行为和业务流相变现象的特性 ,讨论了相变现象的存在对于网络系统的行为和控制带来的影响 ,并提出了一种改进的能够真实地反映出汇聚业务流的基本统计物理特征的网络业务流模型 。

基于Internet的交通道口事故监测管理系统的研究

传统的道路交通监测存在不足之处 ,相应的路口自动监测力量犹显薄弱。对诸如闯红灯 ,闯单禁行线 ,超速等交通违章行为缺乏现代化的监测、取证等必要手段。笔者提出基于Internet的道路交通违章监测系统 ,有效地利用先进的通信及计算机技术 ,为提高交通道口的管理水平 ,确认违章事故提供了手段 ,并对此系统进行深入地分析和设计 ,同时通过实例证明 ,该系统违章监测和事故确认具有的有效性和可操作性。

基于流量大数据采集分析的互联网流量疏导策略研究与实践

随着我国数字经济的发展,互联网流量呈现稳步增长态势。针对互联网流量分布不均,将影响网络长期稳定发展的问题,论述了互联网流量获取、识别的方法,构建了实现流量采集的网络架构,以及完成流量分析的软件架构,提出了调节疏导互联网流量的可行性市场化策略。该策略经过运营商的实践,已经取得了初步效果,为引导互联网流量合理分布提供了解决思路和参考依据。

表征·畸变·坚守:流量时代新闻评论的再审视

随着新媒体的快速发展和广泛应用,“流量至上”的价值理念成为互联网场域中个体和组织追求的目标。为获取流量,新闻评论的内容生产呈现出主题全面化、主体更丰富、语言网络化、时效性更强、形式视频化等特点。同时,也出现了公共意识淡薄、逻辑连贯性被打破、深刻内涵难以表达、严肃性被消解、效果不佳等问题。因此,流量时代新闻评论既要考虑现实需求,又要突出专业性,更要坚守社会责任,在选题上应聚焦痛点、回应关切;在立意上,应以人为本,高瞻远瞩;在话语上,应亲民通俗,巧用热词;在叙事上,应从微观切入,以小见大;在呈现上,应以内容为主,技术为辅。

基于SVM和聚类的Internet流识别方法

利用支持向量机与聚类结合的方法,通过训练具有不同的特点几类应用对网络流量进行分类,这些应用是从校园网的骨干网中抓获的。鉴别选择算法的设计是用来获取用以进行分类的最佳组合特征。基于无偏向性的训练和测试样本,优化方法拥有比较高的准确性。所有的特征参数从实时捕获的数据包包头中计算出来的,显示出实时网络流量的高精度分类是可以实现的。实验结果表明该方法是有效的。

基于堆叠长短期记忆网络的互联网流量预测

对互联网流量的有效预测是网络运营商进行网络优化的重要组成部分,为了提高互联网流量的有效预测,本文提出了一种堆叠长短期记忆网络(LSTM)模型来预测未来的互联网流量。首先,介绍了堆叠LSTM的网络结构。然后构建了4种不同堆叠层数的LSTM网络结构,并利用欧洲11个城市的ISP互联网流量和英国学术网流量数据集进行实验对比,确定了当堆叠层数为2层时,预测的精确度最高。最后为了验证该模型的有效性,通过均方根误差和平均绝对误差这两个衡量标准,与DBN、GRU、HA模型进行比较。实验结果表明,基于堆叠的LSTM预测模型较其它模型的RMSE值和ER值均有所降低。