Intelligent transportation systems aim to provide innovative applications and services relating to traffic management and enable ease of access to information for various system users. The intent to utilize the excessive on-board resources in the transportation system, along with the latest computing resource management technology in conventional clouds, has cultivated the concept of the Vehicular Cloud. Evolved from Vehicular Networks, the vehicular cloud can be formed by vehicles autonomously, and provides a large number of applications and services that can benefit the entire transportation system, as well as drivers, passengers, and pedestrians. However, due to high traffic mobility, the vehicular cloud is built on dynamic physical resources; as a result, it experiences several inherent challenges, which increase the complexity of its implementations. Having a detailed picture of the number of vehicles, as well as their time of availability in a given region through a model, works as a critical stepping stone for enabling vehicular clouds, as well as any other system involving vehicles moving over the traffic network. The number of vehicles represents the amount of computation capabilities available in this region and the navigation time indicates the period of validity for a specific compute node. Therefore, in this thesis, we carry out a comprehensive stochastic analysis of several traffic characteristics related to the implementation of vehicular cloud inside a road segment by adopting proper traffic models. According to the analytical results, we demonstrate the feasibility of running a certain class of applications or services on the vehicular cloud, even for highly dynamic scenarios. Specifically, two kinds of traffic scenarios are modeled: free-flow traffic and queueing-up traffic. We use a macroscopic traffic model to investigate the free-flow traffic and analyze the features such as traffic density, the number of vehicles and their residence time. Also, we utilize the queueing theory to model the queueing-up traffic; the queue length and the waiting time in the queue are analyzed. The results show the boundaries on enabling vehicular cloud, allowing to determine a range of parameters for simulating vehicular clouds.

Several traffic characterization studies have been performed on wireless LANs with the main objective of realizing good and accurate models of the errors in the wireless channel. These models have been extended to model the effect of errors on higher layer protocols, mainly at the data link layer. However, no prior work has been done to study the application level characteristics of MPEG-4 video traffic over 802.11b wireless networks. In this thesis a traffic characterization study of MPEG-4 video traffic over IEEE 802.11b wireless LANs with the main goal of building a tool for software testing is performed. Using two freely available tools to send and receive real-time streams and collect and analyze traces, MPEG-4 encoded video frames are sent over a 11 Mbps, 802.11b wireless LAN to characterize the errors in the channel and the effect of those errors on the quality of the movie. The results of this traffic characterization were modeled using ARTA (Auto Regressive-To-Anything) software. These modeled characteristics were then used to build a tool that generates synthetic traffic emulating real wireless network scenario. The tool emulates the error length and error free length characteristics of the wireless network for the MPEG-4 video traffic using the corresponding modeled characteristics generated by ARTA. The tool can be used by software developers to test their MPEG-4 streaming media applications without the need of the real infrastructure. The tool can also be trained and extended to support testing of any streaming media applications.

Title from PDF of title page. / Document formatted into pages; contains 65 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Several traffic characterization studies have been performed on wireless LANs with the main objective of realizing good and accurate models of the errors in the wireless channel. These models have been extended to model the effect of errors on higher layer protocols, mainly at the data link layer. However, no prior work has been done to study the application level characteristics of MPEG-4 video traffic over 802.11b wireless networks. In this thesis a traffic characterization study of MPEG-4 video traffic over IEEE 802.11b wireless LANs with the main goal of building a tool for software testing is performed. Using two freely available tools to send and receive real-time streams and collect and analyze traces, MPEG-4 encoded video frames are sent over a 11 Mbps, 802.11b wireless LAN to characterize the errors in the channel and the effect of those errors on the quality of the movie. The results of this traffic characterization were modeled using ARTA (Auto Regressive-To-Anything) software. These modeled characteristics were then used to build a tool that generates synthetic traffic emulating real wireless network scenario. The tool emulates the error length and error free length characteristics of the wireless network for the MPEG-4 video traffic using the corresponding modeled characteristics generated by ARTA. The tool can be used by software developers to test their MPEG-4 streaming media applications without the need of the real infrastructure. The tool can also be trained and extended to support testing of any streaming media applications. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Orientador: Leonardo de Souza Mendes / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-04T22:08:02Z (GMT). No. of bitstreams: 1 ProencaJunior_MarioLemes_D.pdf: 8677403 bytes, checksum: 88cbecee7fe9bc3b21f5ce7841dac81c (MD5) Previous issue date: 2005 / Resumo: Nesta tese é apresentado o modelo BLGBA (baseline GBA), que se destina a geração de baseline para segmentos de rede. O modelo foi desenvolvido e implementado na ferramenta Gerenciamento de Backbone Automatizado (GBA) que se destina a auxiliar no gerenciamento de redes. Ele apresenta como seu maior beneficio a geração automática de baseline, com base em análises realizadas de objetos residentes nas MIBs, de agentes SNMP, contidos nos equipamentos de rede. Um estudo sobre trabalhos relacionados ao tema desta tese, referente à caracterização de tráfego e a detecção de anomalias para auxiliar no gerenciamento de redes, também é exposto neste trabalho. Outra contribuição desta tese é o sistema para detecção de anomalias (ADGBA), que utiliza o baseline gerado pelo modelo BLGBA, em conjunto com o movimento coletado em tempo real, nos segmentos de rede. O objetivo principal é informar ao administrador da rede, somente no caso de ocorrência de algum evento significativo não previsto pelo baseline. Para validação do modelo BLGBA e do sistema ADGBA foram realizados testes analíticos e práticos, com dados reais, coletados das redes da Universidade Estadual de Londrina e da Universidade Estadual de Campinas. Os resultados obtidos mostraram tanto a validade do modelo quanto à eficiência do sistema, proporcionando de forma prática e objetiva vantagens significativas para gerência de redes / Abstract: In this thesis the BLGBA (GBA Baseline) model is presented, which intends to create a baseline for network segments. The model was developed and implemented using the GBA tool, which is used as an aid in network management. The major advantage of this model is the automatic generation of the baseline. The baseline was generated based on analyses of SNMP objects of network equipment MIBs. A study about related works to the subject of this thesis is presented, referring to the traffic characterization and anomalies detection aiming to help network management. Another contribution ofthis thesis is the anomalies detection system (ADGBA), that use the baseline generated by BLGBA model and the real movement collected in real time of the network segments. The main objective is to inform the administrator only in case of occurrences of significant events not foreseen by the baseline. Analytical and practical tests have been carried out using real data collected from the State University of Londrina and State University of Campinas networks, aiming to evaluate the BLGBA mode} and ADGBA system. The obtained results shown the validate of the model as also the efficiency of the system and show in practice significant advantages in network management / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

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 to a specific protocol and detecting an intrusion in a computer system, we adopt an Intrusion Detection System (IDS) technique to detect P2P traffic. Our method is a passive detection procedure that uses traffic flows gathered from border routers. Hence, it is scalable and does not have the problems of other approaches that rely on packet payload data or transport layer ports.

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 the CICIDS2017 which is about the same level as the results from previous works on CICIDS2017 (without Zeek). The best performing algorithms were K-Nearest Neighbors, Random Forest and Decision Tree. / IT-säkerhet är ett växande fält inom IT-sektorn. I takt med att allt fler saker ansluts till internet, ökar även angreppsytan och risken för IT-attacker. Ett Nätverksbaserat Intrångsdetekteringssystem (NIDS) kan användas för att upptäcka skadlig trafik i nätverk och maskininlärning har blivit ett allt vanligare sätt att förbättra denna förmåga. I det här examensarbetet används ett NIDS som heter Zeek för att extrahera parametrar baserade på tid och datastorlek från nätverkstrafik. Dessa parametrar analyseras sedan med maskininlärning i Scikit-Learn för att upptäcka skadlig trafik. För datasetet CICIDS2017 uppnåddes en Bayesian detection rate på 98.58% vilket är på ungefär samma nivå som resultat från tidigare arbeten med CICIDS2017 (utan Zeek). Algoritmerna som gav bäst resultat var K-Nearest Neighbors, Random Forest och Decision Tree.

Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing techniques, namely Bayesian blocks and Knuth’s algorithms, over time-tagged events and compares them in the context of rate-based anomaly detection. This was accomplished using data from experimental RFID readings and comparing (1) the event counts versus time if using the smoothed curves versus empirical histograms of the raw data and (2) the threshold-dependent alert-rates based on inter-arrival times obtained if using the smoothed curves versus that of the raw data itself. Results indicate that both algorithms adequately model RFID traffic in which inter-event time statistics are stationary but that Bayesian blocks become superior for traffic in which such statistics experience abrupt changes.