Network security is a complex field that is handled by security professionals who need certain expertise and experience to configure security systems. With the ever increasing size of the networks, managing them is going to be a daunting task. What kind of solution can be used to generate effective security configurations by both security professionals and nonprofessionals alike? In this thesis, a web tool is developed to simplify the process of configuring security systems by translating direct human language input into meaningful, working security rules. These human language inputs yield the security rules that the individual wants to implement in their network. The human language input can be as simple as, "Block Facebook to my son's PC". This tool will translate these inputs into specific security rules and install the translated rules into security equipment such as virtualized Cisco FWSM network firewall, Netfilter host-based firewall, and Snort Network Intrusion Detection. This tool is implemented and tested in both a traditional network and a cloud environment. One thousand input policies were collected from various users such as staff from UNT departments' and health science, including individuals with network security background as well as students with a non-computer science background to analyze the tool's performance. The tool is tested for its accuracy (91%) in generating a security rule. It is also tested for accuracy of the translated rule (86%) compared to a standard rule written by security professionals. Nevertheless, the network security tool built has shown promise to both experienced and inexperienced people in network security field by simplifying the provisioning process to result in accurate and effective network security rules.

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The objective of this thesis is creating a design of a redundant secure VPN WAN network for Medical Emergency Service of Pardubice region. The starting point of this thesis is the analysis of the current state of the corporate computer network, which was evaluated as not satisfying. The result is a design of WAN network, which satisfies the requirements of the investor. The solution also includes the project of implementation and financial calculation of the project.

The thesis focuses on the management and functionality of the Network Operations Center, whose function is to maintain optimal network operations on various platforms, media and communication channels. The department is in a company that develops AAA game titles. The first part defines the theoretical basis. The next section introduces the company together with the analysis of the functioning of the department and communication. The last part is devoted to the design of the organizational structure, which includes the process of creating a new job. The process involves the division of activities, the recruitment process and the economic appreciation itself.

<p>Through the computing advance and GPU (Graphics Processing Unit) availability for math calculation, the deep learning field becomes more popular and prevalent. Object detection with deep learning, which is the part of image processing, plays an important role in automatic vehicle drive and computer vision. Object detection includes object localization and object classification. Object localization involves that the computer looks through the image and gives the correct coordinates to localize the object. Object classification is that the computer classification targets into different categories. The traditional image object detection pipeline idea is from Fast/Faster R-CNN [32] [58]. The region proposal network generates the contained objects areas and put them into classifier. The first step is the object localization while the second step is the object classification. The time cost for this pipeline function is not efficient. Aiming to address this problem, You Only Look Once (YOLO) [4] network is born. YOLO is the single neural network end-to-end pipeline with the image processing speed being 45 frames per second in real time for network prediction. In this thesis, the convolution neural networks are introduced, including the state of art convolutional neural networks in recently years. YOLO implementation details are illustrated step by step. We adopt the YOLO network for our applications since the YOLO network has the faster convergence rate in training and provides high accuracy and it is the end to end architecture, which makes networks easy to optimize and train. </p>

This thesis consists of two projects in the field of machine learning. Previous research in the OSCAR UNT lab based on KMeans color quantization is further developed and applied to individual color channels and segmented input images to explore compression rates while still maintaining high output image quality. The second project implements a small-scale dual path network for image classifiaction utilizing the CIFAR-10 dataset containing 60,000 32x32 pixel images ranging across ten categories.

Analysis of Inferred Social Networks While the social network analysis (SNA) is not a new science branch, thanks to the boom of social media platforms in recent years new methods and approaches appear with increasing frequency. However, not all datasets have network structure visible at first glance. We believe that every reasonable interconnected system of data hides a social network, which can be inferred using specific methods. In this thesis we examine such social network, inferred from the real-world data of a smaller bank. We also review some of the most commonly used methods in SNA and then apply them on our complex network, expecting to find structures typical for traditional social networks.

<div>Advances in virtualization technologies have revolutionized the design of the core of cellular networks. However, the adoption of microservice design patterns and migration of services from purpose-built hardware to virtualized hardware has adversely affected the delivery of latency-sensitive services.</div><div><br></div><div>In this dissertation, we make a case for cloud-native (microservice container packaged) network functions in the cellular core by proposing domain knowledge-driven, traffic-aware, orchestration frameworks to make network placement decisions. We begin by evaluating the suitability of virtualization technologies for the cellular core and demonstrating that container-driven deployments can significantly outperform other virtualization technologies such as Virtual Machines for control and data plane applications.</div><div><br></div><div>To support the deployment of latency-sensitive applications on virtualized hardware, we propose using Virtual Network Function (VNF) bundles (aggregates) to handle transactions. Specifically, we design Invenio to leverage a combination of network traces and domain knowledge to identify VNFs involved in processing a specific transaction, which are then collocated by a traffic-aware orchestrator. By ensuring that a user request is processed by a single aggregate of collocated VNFs, Invenio can significantly reduce end-to-end latencies and improve user experience.</div><div><br></div><div>Finally, to understand the challenges in using container-driven deployments in real-world applications, we develop and evaluate a novel caller-ID spoofing detection solution in Voice over LTE (VoLTE) calls. Our proposed solution, NASCENT, cross validates the caller-ID used during voice-call signaling with a previously authenticated caller-ID to detect caller-ID spoofing. Our evaluation with traditional and container-driven deployments shows that container-driven deployment can not only support complex cellular services but also outperform traditional deployments.</div><div><br></div>

Conventional wireless techniques are becoming inadequate for beyond fifth-generation (5G) networks due to latency and bandwidth considerations. To increase the wireless network throughput and improve wireless communication systems’ error performance, we propose physical layer network coding (PNC) in an Intelligent Reflecting Surface (IRS)-assisted environment. We consider an IRS-aided butterfly network, where we propose an algorithm for obtaining the optimal IRS phases. Also, analytic expressions for the bit error rate (BER) are derived. The numerical results demonstrate that the scheme proposed in this thesis significantly enhances the BER performance. The proposed scheme is compared to traditional network coding without IRS. For instance, at a target BER of 10−3, 28 dB and 0.75 dB signal to noise ratio (SNR) gains are achieved at the relay and destination node of the 32-element IRS-assisted butterfly network model.

Vehicle to Vehicle (V2V) communication provides a flexible and real-time information dissemination mechanism through various applications of Intelligent Transportation Systems (ITS). Achieving seamless connectivity through multi-hop vehicular communication with sparse network is a challenging issue. In this paper, we have studied this multi-hop vehicular connectivity in an urban scenario using GPS traces obtained from San Francisco Yellow cabs. Our current work describes a new algorithm for the analysis of topological properties like connectivity and partitions for any kind of vehicular or mobile computing environment. The novel approach uses bitwise manipulation of sparse matrix with an efficient storage technique for determining multi-hop connectivity. The computation mechanism can be further scaled to parallel processing environment. The main contribution of this research is threefold. First, developing an efficient algorithm to quantify multi-hop connectivity with the aid of bitwise manipulation of sparse matrix. Second, investigating the time varying nature of multi-hop vehicular connectivity and dynamic network partitioning of the topology. Third, deriving a mathematical model for calculating message propagation rate in an urban environment.