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Thematische Bibliographien / Network traffic detection / Dissertationen

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Dissertationen zum Thema „Network traffic detection“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Brauckhoff, Daniela. "Network traffic anomaly detection and evaluation." Aachen Shaker, 2010. http://d-nb.info/1001177746/04.

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2

Udd, Robert. "Anomaly Detection in SCADA Network Traffic." Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122680.

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Critical infrastructure provides us with the most important parts of modern society, electricity, water and transport. To increase efficiency and to meet new demands from the customer remote monitoring and control of the systems is necessary. This opens new ways for an attacker to reach the Supervisory Control And Data Acquisition (SCADA) systems that control and monitors the physical processes involved. This also increases the need for security features specially designed for these settings. Anomaly-based detection is a technique suitable for the more deterministic SCADA systems. This thesis

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Yellapragada, Ramani. "Probabilistic Model for Detecting Network Traffic Anomalies." Ohio University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1088538020.

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4

Zhang, Junjie. "Effective and scalable botnet detection in network traffic." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44837.

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Botnets represent one of the most serious threats against Internet security since they serve as platforms that are responsible for the vast majority of large-scale and coordinated cyber attacks, such as distributed denial of service, spamming, and information stolen. Detecting botnets is therefore of great importance and a number of network-based botnet detection systems have been proposed. However, as botnets perform attacks in an increasingly stealthy way and the volume of network traffic is rapidly growing, existing botnet detection systems are faced with significant challenges in terms of

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Babaie, Tahereh Tara. "New Methods for Network Traffic Anomaly Detection." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12032.

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In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel m

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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

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Dandurand, Luc. "Detection of network infrastructure attacks using artificial traffic." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq44906.pdf.

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8

Gupta, Vikas. "File Detection in Network Traffic Using Approximate Matching." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22696.

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Virtually every day data breach incidents are reported in the news. Scammers, fraudsters, hackers and malicious insiders are raking in millions with sensitive business and personal information. Not all incidents involve cunning and astute hackers. The involvement of insiders is ever increasing. Data information leakage is a critical issue for many companies, especially nowadays where every employee has an access to high speed internet.In the past, email was the only gateway to send out information but with the advent of technologies like SaaS (e.g. Dropbox) and other similar services, possible

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Brauckhoff, Daniela [Verfasser]. "Network Traffic Anomaly Detection and Evaluation / Daniela Brauckhoff." Aachen : Shaker, 2010. http://d-nb.info/1122546610/34.

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10

Taggart, Benjamin T. "Incorporating neural network traffic prediction into freeway incident detection." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=723.

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Thesis (M.S.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains viii, 55 p. : ill. (some col.) Vita. Includes abstract. Includes bibliographical references (p. 52-55).

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Kakavelakis, Georgios. "A real-time system for abusive network traffic detection." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5754.

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Approved for public release; distribution is unlimited<br>Abusive network traffic--to include unsolicited e-mail, malware propagation, and denial-of-service attacks--remains a constant problem in the Internet. Despite extensive research in, and subsequent deployment of, abusive-traffic detection infrastructure, none of the available techniques addresses the problem effectively or completely. The fundamental failing of existing methods is that spammers and attack perpetrators rapidly adapt to and circumvent new mitigation techniques. Analyzing network traffic by exploiting transport-layer ch

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Moe, Lwin P. "Cyber security risk analysis framework : network traffic anomaly detection." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118536.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 84-86).<br>Cybersecurity is a growing research area with direct commercial impact to organizations and companies in every industry. With all other technological advancements in the Internet of Things (IoT), mobile devices, cloud computing, 5G network, and artificial intelligence, the need for cybersecurity is more critical than ever before. These technologies drive the need for tight

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Carlsson, Oskar, and Daniel Nabhani. "User and Entity Behavior Anomaly Detection using Network Traffic." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14636.

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14

Caulkins, Bruce. "SESSION-BASED INTRUSION DETECTION SYSTEM TO MAP ANOMALOUS NETWORK TRAFFIC." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3466.

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Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal  firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other

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LUO, SONG. "CREATING MODELS OF INTERNET BACKGROUND TRAFFIC SUITABLE FOR USE IN EVALUATING NETWORK INTRUSION DETECTION SYSTEMS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2790.

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This dissertation addresses Internet background traffic generation and network intrusion detection. It is organized in two parts. Part one introduces a method to model realistic Internet background traffic and demonstrates how the models are used both in a simulation environment and in a lab environment. Part two introduces two different NID (Network Intrusion Detection) techniques and evaluates them using the modeled background traffic. To demonstrate the approach we modeled five major application layer protocols: HTTP, FTP, SSH, SMTP and POP3. The model of each protocol includes an empirical

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Cowan, KC Kaye. "Detecting Hidden Wireless Cameras through Network Traffic Analysis." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/100148.

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Wireless cameras dominate the home surveillance market, providing an additional layer of security for homeowners. Cameras are not limited to private residences; retail stores, public bathrooms, and public beaches represent only some of the possible locations where wireless cameras may be monitoring people's movements. When cameras are deployed into an environment, one would typically expect the user to disclose the presence of the camera as well as its location, which should be outside of a private area. However, adversarial camera users may withhold information and prevent others from discove

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Ramadas, Manikantan. "Detecting Anomalous Network Traffic With Self-Organizing Maps." Ohio University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1049472005.

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18

Kim, Seong Soo. "Real-time analysis of aggregate network traffic for anomaly detection." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2312.

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The frequent and large-scale network attacks have led to an increased need for developing techniques for analyzing network traffic. If efficient analysis tools were available, it could become possible to detect the attacks, anomalies and to appropriately take action to contain the attacks before they have had time to propagate across the network. In this dissertation, we suggest a technique for traffic anomaly detection based on analyzing the correlation of destination IP addresses and distribution of image-based signal in postmortem and real-time, by passively monitoring packet headers of tra

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El-Shehaly, Mai Hassan. "A Visualization Framework for SiLK Data exploration and Scan Detection." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/34606.

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Network packet traces, despite having a lot of noise, contain priceless information, especially for investigating security incidents or troubleshooting performance problems. However, given the gigabytes of flow crossing a typical medium sized enterprise network every day, spotting malicious activity and analyzing trends in network behavior becomes a tedious task. Further, computational mechanisms for analyzing such data usually take substantial time to reach interesting patterns and often mislead the analyst into reaching false positives, benign traffic being identified as malicious, or false

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Riddell, Liam R. "Heterogeneous anomaly detection from network traffic streams using data summarization." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2599.

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The extreme volumes of modern networks and the increasing demands on security professionals present a critical need for analysis efficiency. Network anomaly summarization combines the broad threat detection characteristics of anomaly detection with the big data reducing qualities of summarization. However, summarising anomalies from network traffic data streams presents numerous obstacles. This thesis proposes a novel attack to anomaly mapping technique for heterogeneous network threat classification and provides a novel auto-encoding latent reflection approach for summarising network anomalie

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Sathyanarayana, Supreeth. "Characterizing the effects of device components on network traffic." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47640.

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When a network packet is formed by a computer's protocol stack, there are many components (e.g., Memory, CPU, etc.) of the computer that are involved in the process. The objective of this research is to identify, characterize and analyze the effects of the various components of a device (e.g., Memory, CPU, etc.) on the device's network traffic by measuring the changes in its network traffic with changes in its components. We also show how this characterization can be used to effectively perform counterfeit detection of devices which have counterfeit components (e.g., Memory, CPU, etc.). To o

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Alizadeh, Hassan. "Intrusion detection and traffic classification using application-aware traffic profiles." Doctoral thesis, Universidade de Aveiro, 2018. http://hdl.handle.net/10773/23545.

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Doutoramento em Engenharia Eletrotécnica no âmbito do programa doutoral MAP-tele<br>Along with the ever-growing number of applications and end-users, online network attacks and advanced generations of malware have continuously proliferated. Many studies have addressed the issue of intrusion detection by inspecting aggregated network traffic with no knowledge of the responsible applications/services. Such systems may detect abnormal tra c, but fail to detect intrusions in applications whenever their abnormal traffic ts into the network normality profiles. Moreover, they cannot identify in

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Syal, Astha. "Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques." Youngstown State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1578259840945109.

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24

Wang, Xiaoming. "Hierarchical TCP network traffic classification with adaptive optimisation." Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/8228.

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Nowadays, with the increasing deployment of modern packet-switching networks, traffic classification is playing an important role in network administration. To identify what kinds of traffic transmitting across networks can improve network management in various ways, such as traffic shaping, differential services, enhanced security, etc. By applying different policies to different kinds of traffic, Quality of Service (QoS) can be achieved and the granularity can be as fine as flow-level. Since illegal traffic can be identified and filtered, network security can be enhanced by employing advance

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Lee, Robert. "ON THE APPLICATION OF LOCALITY TO NETWORK INTRUSION DETECTION: WORKING-SET ANALYSIS OF REAL AND SYNTHETIC NETWORK SERVER TRAFFIC." Doctoral diss., Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002718.

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26

Palmieri, Francesco. "Network anaomaly detection based on the observation of multi-scale traffic dynamics." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/370.

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2010 - 2011<br>Con la sempre più rapida crescita in termini di dimensioni e complessità delle moderne infrastrutture di rete, l’attività di individuazione e prevenzione di abusi e violazioni nell’uso di tali infrastrutture sta diventando sempre più strategica per garantire alle stesse un adeguato grado di protezione sia dall’esterno che da possibili minacce interne. In questo particolare scenario stanno emergendo prepotentemente molte tecniche per il controllo automatizzato del traffico di rete e basate sulla formulazione di modelli di comportamento normali o anomali del traffico stesso, al

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Minton, Carl Edward. "Modeling and Estimation Techniques for Wide-Area Network Traffic with Atypical Components." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/32044.

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A critical first step to improving existing and designing future wide-area networks is an understanding of the load placed on these networks. Efforts to model traffic are often confounded by atypical traffic - traffic particular to the observation site not ubiquitously applicable. The causes and characteristics of atypical traffic are explored in this thesis. Atypical traffic is found to interfere with parsimonious analytic traffic models. A detection and modeling technique is presented and studied for atypical traffic characterized by strongly clustered inliers. This technique is foun

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GIORDANO, DANILO. "Machine Learning and Big Data Methodologies for Network Traffic Monitoring." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2668398.

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Over the past 20 years, the Internet saw an exponential grown of traffic, users, services and applications. Currently, it is estimated that the Internet is used everyday by more than 3.6 billions users, who generate 20 TB of traffic per second. Such a huge amount of data challenge network managers and analysts to understand how the network is performing, how users are accessing resources, how to properly control and manage the infrastructure, and how to detect possible threats. Along with mathematical, statistical, and set theory methodologies machine learning and big data approaches have emer

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Soysal, Murat. "A Novel Method For The Detection Of P2p Traffic In The Network Backbone Inspired By Intrusion Detection Systems." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12607315/index.pdf.

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The share of peer-to-peer (P2P) protocol in the total network traffic grows dayby- day in the Turkish Academic Network (UlakNet) similar to the other networks in the world. This growth is mostly because of the popularity of the shared content and the great enhancement in the P2P protocol since it first came out with Napster. The shared files are generally both large and copyrighted. Motivated by the problems of UlakNet with the P2P traffic, we propose a novel method for P2P traffic detection in the network backbone in this thesis. Observing the similarity between detecting traffic that belongs

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Casas, Hernandez Pedro. "Statistical analysis of network traffic for anomaly detection and quality of service provisioning." Télécom Bretagne, 2010. http://www.theses.fr/2010TELB0111.

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Traditionnellement, la gestion du trafic en cœur de réseau repose sur le surdimensionnement pour simplifier les opérations de gestion. Cependant, étant donnés la grande variabilité et l'hétérogénéité du trafic actuel, la montée en puissance d'applications qui nécessitent de la Qualité de Service, et le déploiement des technologies à très haut débit dans l'accès au réseau, il est nécessaire de développer des techniques d'ingénierie qui optimisent l'utilisation des ressources déployées. En particulier, il est nécessaire de concevoir une ingénierie de réseau qui s'appuie sur la mesure du trafic.

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SORO, FRANCESCA. "An AI and data-driven approach to unwanted network traffic inspection." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2950486.

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Wang, Qinghua. "Traffic analysis, modeling and their applications in energy-constrained wireless sensor networks on network optimization and anomaly detection /." Doctoral thesis, Sundsvall : Tryckeriet Mittuniversitetet, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-10690.

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Khasgiwala, Jitesh. "Analysis of Time-Based Approach for Detecting Anomalous Network Traffic." Ohio University / OhioLINK, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1113583042.

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Rabadia, Priya Naran. "Extraction of patterns in selected network traffic for a precise and efficient intrusion detection approach." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2018. https://ro.ecu.edu.au/theses/2142.

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This thesis investigates a precise and efficient pattern-based intrusion detection approach by extracting patterns from sequential adversarial commands. As organisations are further placing assets within the cyber domain, mitigating the potential exposure of these assets is becoming increasingly imperative. Machine learning is the application of learning algorithms to extract knowledge from data to determine patterns between data points and make predictions. Machine learning algorithms have been used to extract patterns from sequences of commands to precisely and efficiently detect adversaries

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Thomas, Kim. "Incident detection on arterials using neural network data fusion of simulated probe vehicle and loop detector data /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18433.pdf.

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Damour, Gabriel. "Information-Theoretic Framework for Network Anomaly Detection: Enabling online application of statistical learning models to high-speed traffic." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252560.

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With the current proliferation of cyber attacks, safeguarding internet facing assets from network intrusions, is becoming a vital task in our increasingly digitalised economies. Although recent successes of machine learning (ML) models bode the dawn of a new generation of intrusion detection systems (IDS); current solutions struggle to implement these in an efficient manner, leaving many IDSs to rely on rule-based techniques. In this paper we begin by reviewing the different approaches to feature construction and attack source identification employed in such applications. We refer to these ste

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Kačic, Matej. "Analýza útoků na bezdrátové sítě." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-412597.

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This work describes security mechanisms of wireless network based on 802.11 standard and security enhancement 802.11i of these networks known as WPA2, where the analysis of vulnerabilities and attacks on these networks were performed. The work discusses two major security issues. The first is unsecure management frames responsible for vulnerability with direct impact on availability and the other is the vulnerability that allows executing the impersonalize type of attacks. The system for generation attacks was designed to realize any attack very fast and efficient. The core of the thesis is th

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Akhlaq, Monis. "Improved performance high speed network intrusion detection systems (NIDS) : a high speed NIDS architectures to address limitations of packet loss and low detection rate by adoption of dynamic cluster architecture and traffic anomaly filtration (IADF)." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5377.

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Intrusion Detection Systems (IDS) are considered as a vital component in network security architecture. The system allows the administrator to detect unauthorized use of, or attack upon a computer, network or telecommunication infrastructure. There is no second thought on the necessity of these systems however; their performance remains a critical question. This research has focussed on designing a high performance Network Intrusion Detection Systems (NIDS) model. The work begins with the evaluation of Snort, an open source NIDS considered as a de-facto IDS standard. The motive behind the eval

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Li, Zhi. "Fuzzy logic based robust control of queue management and optimal treatment of traffic over TCP/IP networks." University of Southern Queensland, Faculty of Sciences, 2005. http://eprints.usq.edu.au/archive/00001461/.

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Improving network performance in terms of efficiency, fairness in the bandwidth, and system stability has been a research issue for decades. Current Internet traffic control maintains sophistication in end TCPs but simplicity in routers. In each router, incoming packets queue up in a buffer for transmission until the buffer is full, and then the packets are dropped. This router queue management strategy is referred to as Drop Tail. End TCPs eventually detect packet losses and slow down their sending rates to ease congestion in the network. This way, the aggregate sending rate converges to the

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Hoelscher, Igor Gustavo. "Detecção e classificação de sinalização vertical de trânsito em cenários complexos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163777.

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A mobilidade é uma marca da nossa civilização. Tanto o transporte de carga quanto o de passageiros compartilham de uma enorme infra-estrutura de conexões operados com o apoio de um sofisticado sistema logístico. Simbiose otimizada de módulos mecânicos e elétricos, os veículos evoluem continuamente com a integração de avanços tecnológicos e são projetados para oferecer o melhor em conforto, segurança, velocidade e economia. As regulamentações organizam o fluxo de transporte rodoviário e as suas interações, estipulando regras a fim de evitar conflitos. Mas a atividade de condução pode tornar-se

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Gustavsson, Vilhelm. "Machine Learning for a Network-based Intrusion Detection System : An application using Zeek and the CICIDS2017 dataset." Thesis, KTH, Hälsoinformatik och logistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253273.

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Cyber security is an emerging ﬁeld in the IT-sector. As more devices are connected to the internet, the attack surface for hackers is steadily increasing. Network-based Intrusion Detection Systems (NIDS) can be used to detect malicious traﬃc in networks and Machine Learning is an up and coming approach for improving the detection rate. In this thesis the NIDS Zeek is used to extract features based on time and data size from network traﬃc. The features are then analyzed with Machine Learning in Scikit-Learn in order to detect malicious trafﬁc. A 98.58% Bayesian detection rate was achieved for t

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Swaro, James E. "A Heuristic-Based Approach to Real-Time TCP State and Retransmission Analysis." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448030769.

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Barabas, Maroš. "Bezpečnostní analýza síťového provozu pomocí behaviorálních signatur." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-412570.

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This thesis focuses on description of the current state of research in the detection of network attacks and subsequently on the improvement of detection capabilities of specific attacks by establishing a formal definition of network metrics. These metrics approximate the progress of network connection and create a signature, based on behavioral characteristics of the analyzed connection. The aim of this work is not the prevention of ongoing attacks, or the response to these attacks. The emphasis is on the analysis of connections to maximize information obtained and definition of the basis of d

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Číp, Pavel. "Detekce a rozpoznávání dopravních značek." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217772.

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The thesis deals with traffic sign detection and recongnition in the urban environment and outside the town. A precondition for implementation of the system is built-in camera, usually in a car rear-view mirror. The camera scans the scene before the vehicle. The image data are transfered to the connected PC, where the data are transformation to information and evalutations. If the sign was detected the system is visually warned the driver. For a successful goal is divided into four separate blocks. The first part is the preparing of the image data. There are color segmentation with knowledge o

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Šišmiš, Lukáš. "Optimalizace IDS/IPS systému Suricata." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445503.

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V dnešnom svete zrýchľujúcej sa sieťovej prevádzky je potrebné držať krok v jej monitorovaní . Dostatočný prehľad o dianí v sieti dokáže zabrániť rozličným útokom na ciele nachádzajúce sa v nej . S tým nám pomáhajú systémy IDS, ktoré upozorňujú na udalosti nájdené v analyzovanej prevádzke . Pre túto prácu bol vybraný systém Suricata . Cieľom práce je vyladiť nastavenia systému Suricata s rozhraním AF_PACKET pre optimálnu výkonnosť a následne navrhnúť a implementovať optimalizáciu Suricaty . Výsledky z meraní AF_PACKET majú slúžiť ako základ pre porovnanie s navrhnutým vylepšením . Navrhovaná o

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Mazel, Johan. "Unsupervised network anomaly detection." Thesis, Toulouse, INSA, 2011. http://www.theses.fr/2011ISAT0024/document.

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La détection d'anomalies est une tâche critique de l'administration des réseaux. L'apparition continue de nouvelles anomalies et la nature changeante du trafic réseau compliquent de fait la détection d'anomalies. Les méthodes existantes de détection d'anomalies s'appuient sur une connaissance préalable du trafic : soit via des signatures créées à partir d'anomalies connues, soit via un profil de normalité. Ces deux approches sont limitées : la première ne peut détecter les nouvelles anomalies et la seconde requiert une constante mise à jour de son profil de normalité. Ces deux aspects limitent

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Korczynski, Maciej. "Classification de flux applicatifs et détection d'intrusion dans le trafic Internet." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00858571.

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Le sujet de la classification de trafic r'eseau est d'une grande importance pourla planification de r'eseau efficace, la gestion de trafic 'a base de r'egles, la gestionde priorit'e d'applications et le contrˆole de s'ecurit'e. Bien qu'il ait re¸cu une atten-tion consid'erable dans le milieu de la recherche, ce th'eme laisse encore de nom-breuses questions en suspens comme, par exemple, les m'ethodes de classificationdes flux de trafics chiffr'es. Cette th'ese est compos'ee de quatre parties. La premi'erepr'esente quelques aspects th'eoriques li'es 'a la classification de trafic et 'a la d'ete

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Sedlo, Ondřej. "Vylepšení Adversariální Klasifikace v Behaviorální Analýze Síťové Komunikace Určené pro Detekci Cílených Útoků." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417204.

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V této práci se zabýváme vylepšením systémů pro odhalení síťových průniků. Konkrétně se zaměřujeme na behaviorální analýzu, která využívá data extrahovaná z jednotlivých síťových spojení. Tyto informace využívá popsaný framework k obfuskaci cílených síťových útoků, které zneužívají zranitelností v sadě soudobých zranitelných služeb. Z Národní databáze zranitelností od NIST vybíráme zranitelné služby, přičemž se omezujeme jen na roky 2018 a 2019. Ve výsledku vytváříme nový dataset, který sestává z přímých a obfuskovaných útoků, provedených proti vybraným zranitelným službám, a také z jejich pro

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Hošták, Viliam Samuel. "Učení se automatů pro rychlou detekci anomálií v síťovém provozu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449296.

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The focus of this thesis is the fast network anomaly detection based on automata learning. It describes and compares several chosen automata learning algorithms including their adaptation for the learning of network characteristics. In this work, various network anomaly detection methods based on learned automata are proposed which can detect sequential as well as statistical anomalies in target communication. For this purpose, they utilize automata's mechanisms, their transformations, and statistical analysis. Proposed detection methods were implemented and evaluated using network traffic of

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Істратов, В. І. "Інформаційна інтелектуальна технологія розпізнавання шкідливого трафіку в мережі інтернету речей". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/74855.

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Кваліфікаційну роботу магістра присвячено розробці інформаційної інтелектуальної технології розпізнавання шкідливого трафіку в мережі Інтернету речей, саме ця технологія і є результатом проведеної роботи. В роботі визначено актуальність проблеми, виконано аналіз моделей, визначено мету та задачі дослідження, обрано методи дослідження, проведено планування робіт, формування та підготовку вхідних даних, навчання та тестування моделі.

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