With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science / Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles.

WiFi networks are susceptible to statistical traffic analysis attacks. Despite encryption, the metadata of encrypted traffic, such as packet inter-arrival time and size, remains visible. This visibility allows potential eavesdroppers to infer private information in the Internet of Things (IoT) environment. For example, it allows for the identification of sleep monitors and the inference of whether a user is awake or asleep. WiFi eavesdropping theoretically enables the identification of IoT devices without the need to join the victim's network. This attack scenario is more realistic and much harder to defend against, thus posing a real threat to user privacy. However, researchers have not thoroughly investigated this type of attack due to the noisy nature of wireless channels and the relatively low accuracy of WiFi sniffers. Furthermore, many countermeasures proposed in the literature are inefficient in addressing side-channel leakage in WiFi networks. They often burden internet traffic with high data overhead and disrupt the user experience by introducing deliberate delays in packet transmission. This dissertation investigates privacy leakage resulting from WiFi eavesdropping and proposes efficient defensive techniques. We begin by assessing the practical feasibility of IoT device identification in WiFi networks. We demonstrate how an eavesdropper can fingerprint IoT devices by passively monitoring the wireless channel without joining the network. After exploring this privacy attack, we introduce a traffic spoofing-based defense within the WiFi channel to protect against such threats. Additionally, we propose a more data-efficient obfuscation technique to counter traffic analytics based on packet size without adding unnecessary noise to the traffic.

As the Internet becomes an ubiquitous medium of communication, it carries more and more malicious activities like spam, worms or denial of service attacks. One solution to detect and collect such malicious traffic is to use honeypots. They are devices or pieces of information that are not part of the usual production system. Their goals are to lure the attackers into a trap to study them, divert their attention from another target or collect statistics. In this work, we propose a lightweight framework to build honeytanks, which are very efficient low-interaction honeypots. We present and evaluate techniques and algorithms to simulate the presence of a large number of hosts with various degrees of realism and scalability, from a completely stateless approach to a stateful approach able, amongst other things, to mimic the behavior of various TCP/IP stacks. Our framework is based on ASAX, a generic and lightweight data stream analyzer. We instantiate ASAX to build powerful traffic handlers. We introduce several extensions to ASAX and to RUSSEL, its programming language. These extensions allow us to develop new concurrent programming techniques to simulate hosts and protocols in a simple and modular way. We use a recently optimized version of ASAX that makes it possible to simulate tens of thousands hosts while keeping the simulation at a high level of realism. To show the benefits of our approach, i.e., greater simplicity, flexibility, and independence of other technologies, we compare our honeytanks to Honeyd and Nepenthes, two well-known low-interaction honeypots.

This work focuses on a cognitive science inspired solution to an event detection problem in a video domain. The thesis raises the question whether video sequences that are taken in highway tunnels can be used to create meaningful data in terms of symbolic representation, and whether these symbolic representations can be used as sequences to be parsed by attribute grammars into abnormal and normal events. The main motivation of the research was to develop a novel algorithm that parses sequences of primitive events created by the image processing algorithms. The domain of the research is video detection and the special application purpose is for highway tunnels, which are critical places for abnormality detection. The method used is attribute grammars to parse the sequences. The symbolic sequences are created from a cascade of image processing algorithms such as / background subtracting, shadow reduction and object tracking. The system parses the sequences and creates alarms if a car stops, moves backwards, changes lanes, or if a person walks into the road or is in the vicinity when a car is moving along the road. These critical situations are detected using Earley&rsquo / s parser, and the system achieves real-time performance while processing the video input. This approach substantially lowers the number of false alarms created by the lower level image processing algorithms by preserving the number of detected events at a maximum. The system also achieves a high compression rate from primitive events while keeping the lost information at minimum. The output of the algorithm is measured against SVM and observed to be performing better in terms of detection and false alarm performance.

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 traffic. This address correlation data are transformed using discrete wavelet transform for effective detection of anomalies through statistical analysis. Results from trace-driven evaluation suggest that the proposed approach could provide an effective means of detecting anomalies close to the source. We present a multidimensional indicator using the correlation of port numbers as a means of detecting anomalies. We also present a network measurement approach that can simultaneously detect, identify and visualize attacks and anomalous traffic in real-time. We propose to represent samples of network packet header data as frames or images. With such a formulation, a series of samples can be seen as a sequence of frames or video. Thisenables techniques from image processing and video compression such as DCT to be applied to the packet header data to reveal interesting properties of traffic. We show that ??scene change analysis?? can reveal sudden changes in traffic behavior or anomalies. We show that ??motion prediction?? techniques can be employed to understand the patterns of some of the attacks. We show that it may be feasible to represent multiple pieces of data as different colors of an image enabling a uniform treatment of multidimensional packet header data. Measurement-based techniques for analyzing network traffic treat traffic volume and traffic header data as signals or images in order to make the analysis feasible. In this dissertation, we propose an approach based on the classical Neyman-Pearson Test employed in signal detection theory to evaluate these different strategies. We use both of analytical models and trace-driven experiments for comparing the performance of different strategies. Our evaluations on real traces reveal differences in the effectiveness of different traffic header data as potential signals for traffic analysis in terms of their detection rates and false alarm rates. Our results show that address distributions and number of flows are better signals than traffic volume for anomaly detection. Our results also show that sometimes statistical techniques can be more effective than the NP-test when the attack patterns change over time.

This master's thesis focuses on 3D reconstruction of vehicles passing in front of a traffic surveillance camera. Calibration process of surveillance camera is first introduced and the relation of automatic calibration with 3D information about observed traffic is described. Furthermore, Structure from Motion, SLAM, and optical flow algorithms are presented. A set of experiments with feature matching and the Structure from Motion algorithm is carried out to examine results on images of passing vehicles. Afterwards, the Structure from Motion pipeline is modified. Instead of using SIFT features, DeepMatching algorithm is utilized to obtain quasi-dense point correspondences for the subsequent reconstruction phase. Afterwards, reconstructed models are refined by applying additional constraints specific to the vehicle reconstruction task. The resultant models are then evaluated. Lastly, observations and acquired information about the process of vehicle reconstruction are utilized to form proposals for prospective design of an entirely custom pipeline that would be specialized for 3D reconstruction of passing vehicles.

The thesis deals with the optimization of website of thai massage studio. This work includes evaluation of the initial state, a proposed amendment, keyword analysis, optimization of texts and support through building backlinks. Evaluation of success is done through the analysis of traffic and tracking the position of the primary keywords on major search engines in the Czech Republic - Google.cz and Seznam.cz.

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Currently, there is a lack of research behind the security of Virtual Reality against fingerprinting attacks and how these affect the Quality of Experience (QoE) and Quality of Service (QoS) for a user. With practical testing in a game which implements traffic shaping methods as security defences, this thesis aims to take the first step towards changing this. Here, tests were made testing QoS and QoE of countermeasures in a VR game using the game-engine Unity. The countermeasures utilized were random padding, random delays and VPNs. The conclusion reached was that using a delay had a significant impact on QoE, creating a high Round-Trip Time, while changing the packet size had minimal impact to both QoE and QoS. Additionally, utilizing a VPN yielded a minimal impact to both the QoE and QoS.

Computer security is to protect the data inside the computer, relay the information, expose the information, or reduce the level of security to some extent. The communication contents are the main target of any malicious intent to interrupt one or more of the three aspects of the information security triad (confidentiality, integrity, and availability). This thesis aims to provide a comprehensive idea of network traffic analysis, various anomaly or intrusion detection systems, the tools used for it, and finally, a comparison of two Network Traffic Analysis (NTA) tools available in the market: Splunk and Security Onion and comparing their finding to analyse their feasibility and efficiency on Anomaly detection. Splunk and Security Onion were found to be different in the method of monitoring, User Interface (UI), and the observations noted. Further scope for future works is also suggested from the conclusions made.