Thematische Bibliographien / Analysis and filtering of network traffic

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Auswahl der wissenschaftlichen Literatur zum Thema „Analysis and filtering of network traffic“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 17. Februar 2022

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Zeitschriftenartikel zum Thema "Analysis and filtering of network traffic"

Rajaboevich, Gulomov Sherzod. „Comparative Analysis of Methods Content Filtering Network Traffic“. International Journal of Emerging Trends in Engineering Research 8, Nr. 5 (25.05.2020): 1561–69. http://dx.doi.org/10.30534/ijeter/2020/15852020.

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Kabala, Piotr, und Dariusz Laskowski. „Analysis of Network Traffic Filtering / Analiza Filtracji Ruchu Sieciowego“. Journal of KONBiN 33, Nr. 1 (01.09.2015): 41–60. http://dx.doi.org/10.1515/jok-2015-0004.

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Abstract The article consist of study on concept of Network Obstacle (firewall, FW) using PLD-Linux platform and available tools, implementation selected mechanism of security, also analysis the effectiveness of prepared Network Obstacle. Study on concept of Network Obstacle is based on Screened Subnet architecture. Testing effectiveness of network Obstacle was found in two fundamental parts. In the first part was examining the security of network obstacle, testing the resistance attempts of scanning port and the most common attacks. The second part of the research meant to show the influence of the Network Obstacle on time and delay of the realization of transaction for HTTP movement depending on level of the security and movement intensity generated by internal network users. Testing the effectiveness of FW was verify by testing the resistance in an attempt of scanning ports and attacks refusal of service (ICMP, SYN Flood), providing base of dispersed attacks DDoS. This part of the research had one aim, to show the influence FW on time and on delay of the realization the transaction for motion HTTP, depending on the security level and intensity of movement generated by the users of the internal network.

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Lu, Yao, Hanhong Jiang, Tao Liao, Chengcheng Xu und Chen Deng. „Characteristic Analysis and Modeling of Network Traffic for the Electromagnetic Launch System“. Mathematical Problems in Engineering 2019 (23.06.2019): 1–7. http://dx.doi.org/10.1155/2019/2929457.

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The electromagnetic launch system (EMLS) is a kind of large-scale complex system with strong mechanical, electrical, and hydraulic coupling. The effective operation of the system requires the support of advanced and reliable health management system, and the function of the health management system relies on the network traffic to complete data transmission and interaction. Through protocol filtering and time-frequency characteristic analysis of network traffic, it is found that the system mainly includes CIP and TCP; traffic of CIP shows continuity and stability, while traffic of TCP shows sparsity and impact. Based on the analysis of traffic characteristics, an SVR-ARMA network traffic model is established, which improves the prediction accuracy and performance of network traffic and provides a reliable technical guarantee for the fault diagnosis and health management of the EMLS.

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Novakov, Stevan, Chung-Horng Lung, Ioannis Lambadaris und Nabil Seddigh. „A Hybrid Technique Using PCA and Wavelets in Network Traffic Anomaly Detection“. International Journal of Mobile Computing and Multimedia Communications 6, Nr. 1 (Januar 2014): 17–53. http://dx.doi.org/10.4018/ijmcmc.2014010102.

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Research into network anomaly detection has become crucial as a result of a significant increase in the number of computer attacks. Many approaches in network anomaly detection have been reported in the literature, but data or solutions typically are not freely available. Recently, a labeled network traffic flow dataset, Kyoto2006+, has been created and is publicly available. Most existing approaches using Kyoto2006+ for network anomaly detection apply various clustering techniques. This paper leverages existing well known statistical analysis and spectral analysis techniques for network anomaly detection. The first popular approach is a statistical analysis technique called Principal Component Analysis (PCA). PCA describes data in a new dimension to unlock otherwise hidden characteristics. The other well known spectral analysis technique is Haar Wavelet filtering analysis. It measures the amount and magnitude of abrupt changes in data. Both approaches have strengths and limitations. In response, this paper proposes a Hybrid PCA–Haar Wavelet Analysis. The hybrid approach first applies PCA to describe the data and then Haar Wavelet filtering for analysis. Based on prototyping and measurement, an investigation of the Hybrid PCA–Haar Wavelet Analysis technique is performed using the Kyoto2006+ dataset. The authors consider a number of parameters and present experimental results to demonstrate the effectiveness of the hybrid approach as compared to the two algorithms individually.

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Lee, Jae-Kook, Taeyoung Hong und Guohua Li. „Traffic and overhead analysis of applied pre-filtering ACL firewall on HPC service network“. Journal of Communications and Networks 23, Nr. 3 (Juni 2021): 192–200. http://dx.doi.org/10.23919/jcn.2021.000011.

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Privalov, Andrey, Vera Lukicheva, Igor Kotenko und Igor Saenko. „Method of Early Detection of Cyber-Attacks on Telecommunication Networks Based on Traffic Analysis by Extreme Filtering“. Energies 12, Nr. 24 (13.12.2019): 4768. http://dx.doi.org/10.3390/en12244768.

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The paper suggests a method of early detection of cyber-attacks by using DDoS attacks as an example) using the method of extreme filtering in a mode close real time. The process of decomposition of the total signal (additive superposition of attacking and legitimate effects) and its decomposition using the method of extreme filtering is simulated. A profile model of a stochastic network is proposed. This allows to specify the influence of the intruder on the network using probabilistic-time characteristics. Experimental evaluation of metrics characterizing the cyber-attack is given. It is demonstrated how obtained values of metrics confirm the process of attack preparation, for instance the large-scaled telecommunication network, which includes the proposed method for early detection of attacks, has a recovery time of no more than 9 s, and the parameters of quality of service remain in an acceptable range.

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Fernández, Diego, Francisco J. Nóvoa, Fidel Cacheda und Víctor Carneiro. „Advancing Network Flow Information Using Collaborative Filtering“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (Dezember 2017): 97–112. http://dx.doi.org/10.1142/s021848851740013x.

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Collaborative Filtering algorithms are frequently employed in e-commerce. However, this kind of algorithms can also be useful in other domains. In an information system thousands of bytes are sent through the network every second. Analyzing this data can require too much time and many resources, but it is necessary for ensuring the right operation of the network. Results are used for profiling, security analysis, traffic engineering and many other purposes. Nowadays, as a complement to a deep inspection of the data, it is more and more common to monitor packet flows, since it consumes less resources and it allows to react faster to any network situation. In a typical ow monitoring system, flows are exported to a collector, which stores the information before being analyzed. However, many collectors work based on time slots, so they do not analyze the flows when they are just received, generating a delay. In this work we demonstrate how Collaborative Filtering algorithms can be applied to this new domain. In particular, using information about past flows, these algorithms can anticipate future flows before being captured. This way, time required for detecting and responding to different network situations is reduced.

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Jain, Sakshi, Mobin Javed und Vern Paxson. „Towards Mining Latent Client Identifiers from Network Traffic“. Proceedings on Privacy Enhancing Technologies 2016, Nr. 2 (01.04.2016): 100–114. http://dx.doi.org/10.1515/popets-2016-0007.

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Abstract Websites extensively track users via identifiers that uniquely map to client machines or user accounts. Although such tracking has desirable properties like enabling personalization and website analytics, it also raises serious concerns about online user privacy, and can potentially enable illicit surveillance by adversaries who broadly monitor network traffic. In this work we seek to understand the possibilities of latent identifiers appearing in user traffic in forms beyond those already well-known and studied, such as browser and Flash cookies. We develop a methodology for processing large network traces to semi-automatically discover identifiers sent by clients that distinguish users/devices/browsers, such as usernames, cookies, custom user agents, and IMEI numbers. We address the challenges of scaling such discovery up to enterprise-sized data by devising multistage filtering and streaming algorithms. The resulting methodology reflects trade-offs between reducing the ultimate analysis burden and the risk of missing potential identifier strings. We analyze 15 days of data from a site with several hundred users and capture dozens of latent identifiers, primarily in HTTP request components, but also in non-HTTP protocols.

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shabtai, Asaf, Dennis Potashnik, Yuval Fledel, Robert Moskovitch und Yuval Elovici. „Monitoring, analysis, and filtering system for purifying network traffic of known and unknown malicious content“. Security and Communication Networks 4, Nr. 8 (26.07.2010): 947–65. http://dx.doi.org/10.1002/sec.229.

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Tian, Zhao, Wei She, Shuang Li, You-Wei Wang, Wei Liu, Guang-Jun Zai, Li-Min Jia, Yong Qin und Hong-Hui Dong. „Key links identification for urban road traffic network based on temporal-spatial distribution of traffic congestion“. Modern Physics Letters B 33, Nr. 25 (10.09.2019): 1950307. http://dx.doi.org/10.1142/s021798491950307x.

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Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy.

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Dissertationen zum Thema "Analysis and filtering of network traffic"

Klečka, Jan. „Monitorovací sonda síťové komunikace“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442398.

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Master thesis deals with analysis of single board PC which use Linux as operation system. Analysis of individual NIDS systems and examined their properties for choosing right candidate for single board computer which shall be used as network probe for analysis, filtering and logging of network traffic. Part of the work is aimed on development of a interface which is used for configuration of network probe through the web browser. Web interface allows perform basic operations over network probe which influence network traffic or specify, which information shall be logged. Subsequently network parsers were implemented for network protocols using the Scappy library. The conclusion of the thesis contains the design of the security cover for the device according to the IP54 requirements.

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Liu, Wei 1975. „Network traffic modelling and analysis“. Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82613.

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In all-photonic networks, both transmission and switching is performed in the optical domain, without optoelectronic conversion for the data traversing the network. An accurate traffic model is critical in an agile all-photonic network (AAPN) which has the ability to dynamically allocate bandwidth to traffic flows as the demand varies.
This thesis focuses on traffic modelling and analysis. A novel traffic model is proposed which can capture the traffic behaviours in all-photonic networks. The new model is based on a study of existing traffic modelling literature. It combines the time-varying Poisson model, gravity model and fractional Gaussian noise. This model can be used for the short-range traffic prediction. We examine Long-Range Dependence and test the time constancy of scaling parameters using the tools designed by Abry and Veitch, to analyze empirical and synthesized traffic traces.

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Simhairi, Nather Zeki. „Traffic assignment and network analysis“. Thesis, Royal Holloway, University of London, 1987. http://repository.royalholloway.ac.uk/items/a3377f99-4ed8-4000-91f8-0384aed4a3c6/1/.

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This thesis studies the transportation network, and is divided into three sections. Initially an algorithm is described which finds the user-equilibrium assignment for networks with linear congestion functions where the cost of travel on a link is dependent on the flow in the whole network. Secondly it investigates the sensitivity of the cost of travel and of the flow distribution in the network, to changes in the link congestion function. Combinatorial methods are used for evaluating the results of the sensitivity analysis. This is done with the aim of obtaining fast and efficient algorithms for the evaluation of cost sensitive and paradoxical links. Finally, for networks where the demand is elastic, it describes the catastrophic behaviour of the point representing the user-equilibrium flow distribution under certain cost conditions.

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Liu, Jian. „Fractal Network Traffic Analysis with Applications“. Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11477.

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Today, the Internet is growing exponentially, with traffic statistics that mathematically exhibit fractal characteristics: self-similarity and long-range dependence. With these properties, data traffic shows high peak-to-average bandwidth ratios and causes networks inefficient. These problems make it difficult to predict, quantify, and control data traffic. In this thesis, two analytical methods are used to study fractal network traffic. They are second-order self-similarity analysis and multifractal analysis. First, self-similarity is an adaptability of traffic in networks. Many factors are involved in creating this characteristic. A new view of this self-similar traffic structure related to multi-layer network protocols is provided. This view is an improvement over the theory used in most current literature. Second, the scaling region for traffic self-similarity is divided into two timescale regimes: short-range dependence (SRD) and long-range dependence (LRD). Experimental results show that the network transmission delay separates the two scaling regions. This gives us a physical source of the periodicity in the observed traffic. Also, bandwidth, TCP window size, and packet size have impacts on SRD. The statistical heavy-tailedness (Pareto shape parameter) affects the structure of LRD. In addition, a formula to estimate traffic burstiness is derived from the self-similarity property. Furthermore, studies with multifractal analysis have shown the following results. At large timescales, increasing bandwidth does not improve throughput. The two factors affecting traffic throughput are network delay and TCP window size. On the other hand, more simultaneous connections smooth traffic, which could result in an improvement of network efficiency. At small timescales, in order to improve network efficiency, we need to control bandwidth, TCP window size, and network delay to reduce traffic burstiness. In general, network traffic processes have a Hlder exponent a ranging between 0.7 and 1.3. Their statistics differ from Poisson processes. From traffic analysis, a notion of the efficient bandwidth, EB, is derived. Above that bandwidth, traffic appears bursty and cannot be reduced by multiplexing. But, below it, traffic is congested. An important finding is that the relationship between the bandwidth and the transfer delay is nonlinear.

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Jiang, Michael Zhonghua. „Analysis of wireless data network traffic“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0012/MQ61444.pdf.

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Heller, Mark D. „Behavioral analysis of network flow traffic“. Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5108.

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Approved for public release, distribution unlimited
Network Behavior Analysis (NBA) is a technique to enhance network security by passively monitoring aggregate traffic patterns and noting unusual action or departures from normal operations. The analysis is typically performed offline, due to the huge volume of input data, in contrast to conventional intrusion prevention solutions based on deep packet inspection, signature detection, and real-time blocking. After establishing a benchmark for normal traffic, an NBA program monitors network activity and flags unknown, new, or unusual patterns that might indicate the presence of a potential threat. NBA also monitors and records trends in bandwidth and protocol use. Computer users in the Department of Defense (DoD) operational networks may use Hypertext Transport Protocol (HTTP) to stream video from multimedia sites like youtube.com, myspace.com, mtv.com, and blackplanet.com. Such streaming may hog bandwidth, a grave concern, given that increasing amounts of operational data are exchanged over the Global Information Grid, and introduce malicious viruses inadvertently. This thesis develops an NBA solution to identify and estimate the bandwidth usage of HTTP streaming video traffic entirely from flow records such as Cisco's NetFlow data.

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Zhang, Yichi. „Residential Network Traffic and User Behavior Analysis“. Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-27001.

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Internet usage is changing and the demands on the broadband networks are ever increasing. So it is still crucial to understand today's network traffic and the usage patterns of the end users, which will lead to more efficient network design, energy and costs savings, and improvement of the service offered to end users. This thesis aims at finding hidden patterns of traffic and user behavior in a residential fiber based access network. To address the problem, a systematic framework of traffic measurement and analysis is developed. It involves PacketLogic traffic data collecting, MySQL database storing, and traffic and user behavior analysis by using Python scripts. Our approach provides new insights on residential network traffic properties and Internet user habits of households, covering topics of aggregated traffic pattern, household traffic modeling, traffic and user penetration for applications, grouping analysis by cluster and subscriber, and concurrent application analysis. The analysis solutions we provide are based on open source tools without proprietary, giving the most flexibility for codes modification and distribution.

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Kreibich, Christian Peter. „Structural traffic analysis for network security monitoring“. Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613090.

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Yu, Han. „Analysis of network traffic in grid system“. Thesis, Loughborough University, 2007. https://dspace.lboro.ac.uk/2134/35162.

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The aim of this work was to conduct experiments to discover the characteristics of the network traffic generated by running Grid applications. In the experiments, the Grid application used was to discover resources and services registered in non-central resource. The Vega Grid was introduced as the experimental Grid platform and Resource Discovery was run in this platform. Since the application of Resource Discovery could generate continuous network traffic, it was useful to measure and analyse this network traffic and find out its characteristics. Several experiments were conducted for the same purpose using three, four, five, and six PCs in different experiments. Moreover, the same experiment using a WAN was conducted with seven PCs (four PCs inside the campus and three PCs outside). Specifically, ETHEREAL was introduced to collect data packets involved with the experiments.

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Vu, Hong Linh. „DNS Traffic Analysis for Network-based Malware Detection“. Thesis, KTH, Kommunikationssystem, CoS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-93842.

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Botnets are generally recognized as one of the most challenging threats on the Internet today. Botnets have been involved in many attacks targeting multinational organizations and even nationwide internet services. As more effective detection and mitigation approaches are proposed by security researchers, botnet developers are employing new techniques for evasion. It is not surprising that the Domain Name System (DNS) is abused by botnets for the purposes of evasion, because of the important role of DNS in the operation of the Internet. DNS provides a flexible mapping between domain names and IP addresses, thus botnets can exploit this dynamic mapping to mask the location of botnet controllers. Domain-flux and fast-flux (also known as IP-flux) are two emerging techniques which aim at exhausting the tracking and blacklisting effort of botnet defenders by rapidly changing the domain names or their associated IP addresses that are used by the botnet. In this thesis, we employ passive DNS analysis to develop an anomaly-based technique for detecting the presence of a domain-flux or fast- flux botnet in a network. To do this, we construct a lookup graph and a failure graph from captured DNS traffic and decompose these graphs into clusters which have a strong correlation between their domains, hosts, and IP addresses. DNS related features are extracted for each cluster and used as input to a classication module to identify the presence of a domain-flux or fast-flux botnet in the network. The experimental evaluation on captured traffic traces veried that the proposed technique successfully detected domain-flux botnets in the traces. The proposed technique complements other techniques for detecting botnets through traffic analysis.
Botnets betraktas som ett av de svåraste Internet-hoten idag. Botnets har använts vid många attacker mot multinationella organisationer och även nationella myndigheters och andra nationella Internet-tjänster. Allt eftersom mer effektiva detekterings - och skyddstekniker tas fram av säkerhetsforskare, har utvecklarna av botnets tagit fram nya tekniker för att undvika upptäckt. Därför är det inte förvånande att domännamnssystemet (Domain Name System, DNS) missbrukas av botnets för att undvika upptäckt, på grund av den viktiga roll domännamnssystemet har för Internets funktion - DNS ger en flexibel bindning mellan domännamn och IP-adresser. Domain-flux och fast-flux (även kallat IP-flux) är två relativt nya tekniker som används för att undvika spårning och svartlistning av IP-adresser av botnet-skyddsmekanismer genom att snabbt förändra bindningen mellan namn och IP-adresser som används av botnets. I denna rapport används passiv DNS-analys för att utveckla en anomali-baserad teknik för detektering av botnets som använder sig av domain-flux eller fast-flux. Tekniken baseras på skapandet av en uppslagnings-graf och en fel-graf från insamlad DNS-traffik och bryter ned dessa grafer i kluster som har stark korrelation mellan de ingående domänerna, maskinerna, och IP-adresserna. DNSrelaterade egenskaper extraheras för varje kluster och används som indata till en klassifficeringsmodul för identiffiering av domain-flux och fast-flux botnets i nätet. Utvärdering av metoden genom experiment på insamlade traffikspår visar att den föreslagna tekniken lyckas upptäcka domain-flux botnets i traffiken. Genom att fokusera på DNS-information kompletterar den föreslagna tekniken andra tekniker för detektering av botnets genom traffikanalys.

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Bücher zum Thema "Analysis and filtering of network traffic"

Peterson, William P. Heavy traffic analysis of a transportation network model. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1994.

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Carrapatoso, E. Traffic analysis for various metropolitan area network topologies. Bradford: University of Bradford. Postgraduate School of Electrical and Electronic Engineering, 1985.

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Kesidis, George. An introduction to communication network analysis. Hoboken, N.J: Wiley-Interscience, 2007.

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library, Wiley online, Hrsg. An introduction to communication network analysis. Hoboken, N.J: Wiley-Interscience, 2007.

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Wein, Lawrence M. Scheduling network of queues: Heavy traffic analysis of multistation network with controllable inputs. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1989.

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B, Mišić Vojislav, Hrsg. Performance modeling and analysis of Bluetooth networks: Polling, scheduling, and traffic control. Boca Raton: Auerbach Publications, 2006.

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Kesidis, George. A course on analysis of communication networks. Hoboken, N.J: John Wiley, 2007.

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Dattatreya, G. R. Performance analysis of queuing and computer networks. Boca Raton: Chapman & Hall/CRC, 2008.

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Dattatreya, G. R. Performance analysis of queuing and computer networks. Boca Raton: CRC Press/Taylor & Francis, 2008.

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Yajuan, Deng, Hrsg. Lu wang huan jing xia gao su gong lu jiao tong shi gu ying xiang chuan bo fen xi yu kong zhi: Traffic accident impact analysis and control of expressway under road network. Beijing: Ke xue chu ban she, 2010.

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Buchteile zum Thema "Analysis and filtering of network traffic"

Pontarelli, Salvatore, und Simone Teofili. „Anti-evasion Technique for Packet Based Pre-filtering for Network Intrusion Detection Systems (Poster)“. In Traffic Monitoring and Analysis, 185–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20305-3_18.

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Gebali, Fayez. „Modeling Network Traffic“. In Analysis of Computer Networks, 445–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15657-6_15.

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Gebali, Fayez. „Modeling Network Traffic“. In Analysis of Computer and Communication Networks, 1–47. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74437-7_11.

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Sadiku, Matthew N. O., und Sarhan M. Musa. „Self-Similarity of Network Traffic“. In Performance Analysis of Computer Networks, 251–65. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01646-7_10.

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Silva, João Marco C., und Solange Rito Lima. „Improving Network Measurement Efficiency through Multiadaptive Sampling“. In Traffic Monitoring and Analysis, 171–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28534-9_18.

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Stefanidis, Kostas, Eirini Ntoutsi, Haridimos Kondylakis und Yannis Velegrakis. „Social-Based Collaborative Filtering“. In Encyclopedia of Social Network Analysis and Mining, 2793–802. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_110171.

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Stefanidis, Kostas, Eirini Ntoutsi, Haridimos Kondylakis und Yannis Velegrakis. „Social-Based Collaborative Filtering“. In Encyclopedia of Social Network Analysis and Mining, 1–9. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_110171-1.

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Giorgi, Giada, und Claudio Narduzzi. „Scaling Analysis of Wavelet Quantiles in Network Traffic“. In Traffic Monitoring and Analysis, 109–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01645-5_13.

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Romirer-Maierhofer, Peter, Fabio Ricciato, Alessandro D’Alconzo, Robert Franzan und Wolfgang Karner. „Network-Wide Measurements of TCP RTT in 3G“. In Traffic Monitoring and Analysis, 17–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01645-5_3.

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Dainotti, Alberto, Antonio Pescapé und Carlo Sansone. „Early Classification of Network Traffic through Multi-classification“. In Traffic Monitoring and Analysis, 122–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20305-3_11.

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Konferenzberichte zum Thema "Analysis and filtering of network traffic"

Del Fiore, Julian M., Pascal Merindol, Valerio Persico, Cristel Pelsser und Antonio Pescape. „Filtering the Noise to Reveal Inter-Domain Lies“. In 2019 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2019. http://dx.doi.org/10.23919/tma.2019.8784618.

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Lambruschini, P., M. Raggio, R. Bajpai und A. Sharma. „Optimized packet pre-filtering for analysis of IP traffic on high-speed networks“. In 2012 International Conference on Signals and Electronic Systems (ICSES 2012). IEEE, 2012. http://dx.doi.org/10.1109/icses.2012.6857541.

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Fouliras, Panayotis. „On RTP filtering for network traffic reduction“. In the 6th International Conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1497185.1497261.

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Kline, Erik, Alexander Afanasyev und Peter Reiher. „Shield: DoS filtering using traffic deflecting“. In 2011 19th IEEE International Conference on Network Protocols (ICNP). IEEE, 2011. http://dx.doi.org/10.1109/icnp.2011.6089077.

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Cerrato, I., M. Leogrande und F. Risso. „Filtering network traffic based on protocol encapsulation rules“. In 2013 International Conference on Computing, Networking and Communications (ICNC 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccnc.2013.6504238.

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Montigny-leboeuf, Annie, und Tim Symchych. „Network Traffic Flow Analysis“. In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277589.

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Abramov, Evgeny, Denis Mordvin und Oleg Makarevich. „Automated method for constructing of network traffic filtering rules“. In the 3rd international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854099.1854141.

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Heard, Nick, Patrick Rubin-Delanchy und Daniel J. Lawson. „Filtering Automated Polling Traffic in Computer Network Flow Data“. In 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC). IEEE, 2014. http://dx.doi.org/10.1109/jisic.2014.52.

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Mistry, Devang, Prasad Modi, Kaustubh Deokule, Aditi Patel, Harshagandha Patki und Omar Abuzaghleh. „Network traffic measurement and analysis“. In 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2016. http://dx.doi.org/10.1109/lisat.2016.7494141.

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Li, Guanyu, Menghao Zhang, Chang Liu, Xiao Kong, Ang Chen, Guofei Gu und Haixin Duan. „NETHCF: Enabling Line-rate and Adaptive Spoofed IP Traffic Filtering“. In 2019 IEEE 27th International Conference on Network Protocols (ICNP). IEEE, 2019. http://dx.doi.org/10.1109/icnp.2019.8888057.

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Berichte der Organisationen zum Thema "Analysis and filtering of network traffic"

Lakhina, Anukool, Konstantina Papagiannaki, Mark Crovella, Christophe Diot, Eric D. Kolaczyk und Nina Taft. Structural Analysis of Network Traffic Flows. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada439086.

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Liu, Jyh-Charn. Progressive Email Classifier (PEC) for Ingress Enterprise Network Traffic Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada534227.

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McCulloh, Ian, Grace Garcia, Kelsey Tardieu, Jennifer MacGibbon, Heather Dye, Kerry Moores, John Graham und Daniel B. Horn. IkeNet: Social Network Analysis of E-mail Traffic in the Eisenhower Leadership Development Program. Fort Belvoir, VA: Defense Technical Information Center, November 2007. http://dx.doi.org/10.21236/ada475212.

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Duvvuri, Sarvani, und Srinivas S. Pulugurtha. Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics. Mineta Transportation Institute, Juli 2021. http://dx.doi.org/10.31979/mti.2021.1946.

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Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.

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Kodupuganti, Swapneel R., Sonu Mathew und Srinivas S. Pulugurtha. Modeling Operational Performance of Urban Roads with Heterogeneous Traffic Conditions. Mineta Transportation Institute, Januar 2021. http://dx.doi.org/10.31979/mti.2021.1802.

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The rapid growth in population and related demand for travel during the past few decades has had a catalytic effect on traffic congestion, air quality, and safety in many urban areas. Transportation managers and planners have planned for new facilities to cater to the needs of users of alternative modes of transportation (e.g., public transportation, walking, and bicycling) over the next decade. However, there are no widely accepted methods, nor there is enough evidence to justify whether such plans are instrumental in improving mobility of the transportation system. Therefore, this project researches the operational performance of urban roads with heterogeneous traffic conditions to improve the mobility and reliability of people and goods. A 4-mile stretch of the Blue Line light rail transit (LRT) extension, which connects Old Concord Rd and the University of North Carolina at Charlotte’s main campus on N Tryon St in Charlotte, North Carolina, was considered for travel time reliability analysis. The influence of crosswalks, sidewalks, trails, greenways, on-street bicycle lanes, bus/LRT routes and stops/stations, and street network characteristics on travel time reliability were comprehensively considered from a multimodal perspective. Likewise, a 2.5-mile-long section of the Blue Line LRT extension, which connects University City Blvd and Mallard Creek Church Rd on N Tryon St in Charlotte, North Carolina, was considered for simulation-based operational analysis. Vissim traffic simulation software was used to compute and compare delay, queue length, and maximum queue length at nine intersections to evaluate the influence of vehicles, LRT, pedestrians, and bicyclists, individually and/or combined. The statistical significance of variations in travel time reliability were particularly less in the case of links on N Tryon St with the Blue Line LRT extension. However, a decrease in travel time reliability on some links was observed on the parallel route (I-85) and cross-streets. While a decrease in vehicle delay on northbound and southbound approaches of N Tryon St was observed in most cases after the LRT is in operation, the cross-streets of N Tryon St incurred a relatively higher increase in delay after the LRT is in operation. The current pedestrian and bicycling activity levels seemed insignificant to have an influence on vehicle delay at intersections. The methodological approaches from this research can be used to assess the performance of a transportation facility and identify remedial solutions from a multimodal perspective.

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Al Hosain, Nourah, und Alma Alhussaini. Evaluating Access to Riyadh’s Planned Public Transport System Using Geospatial Analysis. King Abdullah Petroleum Studies and Research Center, Juni 2021. http://dx.doi.org/10.30573/ks--2021-dp10.

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The King Abdulaziz Project for Public Transport in Riyadh city is one of the world’s largest urban transit systems being developed. The project aims to meet the demands of the city’s growing urban population while reducing traffic congestion, heavy private car dependence and air pollution. The performance of any public transport system largely depends on its accessibility. Therefore, this study evaluates the populations’ access to Riyadh’s public transport stations using network analysis tools based on geographic information systems.

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