GNSS positioning has become a key element in everyday life of millions of people, from the person using google maps to move around an unknown city to the mailman or the DRON pilot who require it to carry out their work. All of them benefit in some way from the GNSS constellations and the position algorithms.The European Union through their GNSS constellation, Galileo, has recently made available a new service called Galileo High Accuracy Service (HAS). With the aim of improving the positioning solutions already provided by the Open Service (OS) to a centimetric level with the target of professional and commercial users requiring this high accuracy. As a result, in this Master Thesis project the steps of the development and implementation of a Software-Defined Radio to collect the High Accuracy corrections transmitted through Galileo GNSS constellation are going to be shown. The SDR itself is going to be made available so that other persons from companies to academia can benefit from it and see how the corrections are extracted and either use the algorithm or implement its own to be able to use this High Accuracy Service.

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Drone-mounted ground penetrating radar (GPR) has the capability to investigate terrain that is inaccessible or hazardous to humans. A linear frequency-modulated (LFM) radar with the potential for GPR applications is described based on the LimeSDR Mini software defined radio (SDR). Challenges of the LimeSDR Mini radar include the SDR's lack of support for transmitter-receiver synchronization and high bleedthrough leakage. These issues are overcome through corrective software processing techniques including deconvolution of the SDR's system impulse response and digital feed-through nulling. Feed-through nulling is effective at reducing bleedthrough leakage, achieving a 26 dB reduction in power. Although high noise can confound the identification of targets with small radar cross sections in dynamic environments, the LimeSDR Mini radar is demonstrated to display a moving target across multiple ranges. This research demonstrates the increasing accessibility of SDR radar for drone applications, as the LimeSDR Mini is lightweight and low-cost compared to high-end SDRs typically used in SDR radar.

The exponentially increasing traffic demand for mobile services requires innovative solutions in both access and backhaul segments of 5th generation (5G) mobile networks. Although, heterogeneous networks (HetNets) are a promising solution for the wireless access, the backhaul segment has received considerably less attention and falls short in meeting the stringent requirements of 5G in terms of capacity and availability. HetNets together with mobility requirements motivate the use of microwave backhauling that supports fiber-like capacity with millimeter-wave communications. However, higher carrier frequencies are subject to weather disturbances like rain that may substantially degrade the network throughput. To mitigate this effect, we develop a fast and accurate rain detection algorithm that triggers a network-layer strategy, i.e., rerouting. The results show that with small detection error the network throughput increases while posing small overhead on the network. The rain impact can be alleviated by centralized rerouting under the software defined networking paradigm. However, careless reconfiguration may impose inconsistency that leads to a significant temporary congestion and limits the gain of rerouting. We propose a consistency-aware rerouting framework by considering the cost of reconfiguration. At each time, the centralized controller may either take a rerouting or no-rerouting decision in order to minimize the total data loss. We use a predictive control algorithm to provide such an online sequence of decisions. Compared to the regular rerouting, our proposed approach reduces the throughput loss and substantially decreases the number of reconfigurations. In the thesis we also study which backhaul option is the best from a techno-economic perspective. We develop a comprehensive framework to calculate the total cost of ownership of the backhaul segment and analyze the profitability in terms of cash flow and net present value. The results highlight the importance of selecting proper backhaul solution to increase profitability. / <p>QC 20170308</p>

For a long time, ships have relied on navigators that could figure out their course andlocation based on seeing objects around them. However, this approach is limited to thenavigators’ ability, and with the increasing number of ships, this job becomes harder andharder. With these aspects in mind, the new system, the Automatic identification system(AIS), was created as a tool to help navigators to navigate and increase safety on the sea.AIS is an automatic identification system and is designed to send out information aboutthe vessel and its location. This thesis looks at the state of the art of Automatic identifica-tion systems and Digital selective calling systems to evaluate the security aspects of thesesystems. The thesis aims to investigate if these two systems are susceptible to spoofingattacks and what resources are required for creating successful attacks. Two experimentswere used to achieve this aim and answer the research questions. The first one was to eval-uate the Automatic identification system and test different spoofing attacks on that system.The second experiment was to test different spoofing attacks on the Digital selective callingsystem. Both of these experiments used two software-defined radios for the experiments.The experiment results show that some of the attacks tested on the systems were success-ful, and the attacks tested were successfully executed against the created system. Theseattacks were created and performed using two software-defined radios to send and receivemessages. To conclude, the two systems are susceptible to spoofing attacks. However, anattacker can gain the necessary information to create spoofing attacks on the systems, withvarying consequences and some limitations.

The advent of Software Defined Networking (SDN) and programmable data planes has revolutionized the networking domain, enabling the programming of networking functions down to the silicon level responsible for data packet switching. Unfortunately, while this programmability offers greater flexibility and control, it also increases the likelihood of introducing software bugs. To counter this risk, rigorous testing methodologies and strategies are essential to ensure the reliability, security, and stability of SDN deployments. A comprehensive approach should combine various techniques, including formal verification, fuzz, and performance testing. Model-Based Testing (MBT) is a technique that can significantly enhance the effectiveness of SDN testing. By leveraging formal models of the system under test, MBT automatically generates test cases that can help identify potential issues in network configuration, data plane programming, and network protocols. Utilizing MBT allows network administrators to systematically explore SDN components’ possible states and transitions, resulting in a higher level of coverage and confidence in the system’s overall stability and security. However, a lack of information on applying MBT in an SDN environment challenges its full implementation and utilization in this field. This master thesis aims to investigate and demonstrate the application of MBT to programmable data plane functions. This work uses VLAN tagging as the target data plane function, and AltWalker is employed as the MBT tool for generating and executing tests on an SDN switch. The results present an initial testing methodology that, when applied to the VLAN tagging function, can provide insights into the potential benefits and challenges of using MBT for SDN testing. This thesis lays the groundwork for further exploration and refinement of MBT methodologies in the context of SDN and programmable data plane functions.

Background:Software-Defined Networking (SDN) represents a network architecture that offers a separate control and data layer, facilitating its rapid deployment and utilization for diverse purposes. However, despite its ease of implementation, SDN is susceptible to numerous security attacks, primarily stemming from its centralized nature. Among these threats, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks pose the most substantial risks. In the event of a successful attack on the SDNcontroller, the entire network may suffer significant disruption. Hence, safe guarding the controller becomes crucial to ensure the integrity and availability of the SDN network. Objectives:This thesis focuses on examining the IP spoofing attack and its impact on the Data Plane, particularly concerning the metrics of an SDN switch. The investigation centers around attacks that manipulate flow-rules to amplify the number of rules and deplete the resources of a switch within the Data Plane of an SDN network. To conduct the study, a software-defined network architecture was constructed using Mininet, with a Ryu controller employed for managing network operations. Various experiments were carried out to observe the response of the SDN system when subjected to an IP spoofing attack, aiming to identify potential mitigation strategies against such threats. Method and Results: To simulate the resource exhaustion scenario on the SDN network’s Data Plane,we deliberately triggered an escalation in the number of flow-rules installed in the switch. This was achieved by sending packets with spoofed IP addresses, there by exploiting the switch’s limited resources. Specifically, we focused on monitoring the impact on CPU utilization, storage memory, latency, and throughput within the switch. Detailed findings were presented in the form of tables, accompanied by graphical representations to visually illustrate the effects of increasing flow rules on the switches. Furthermore, we explored potential mitigation measures by developing an application that actively monitors the flow rules on the Ryu controller, aiming to detect and counteract such resource-exhausting effects.

Digital transformation will experience massive connections and massive data handling. This will imply a growing demand for computing in communication networks due to network softwarization. Moreover, digital transformation will host very sensitive verticals, requiring high end-to-end reliability and low latency. Accordingly, the emerging concept “in-network computing” has been arising. This means integrating the network communications with computing and also performing computations on the transport path of the network. This can be used to deliver actionable information directly to end users instead of raw data. However, this change of paradigm to in-network computing raises disruptive challenges to the current communication networks. In-network computing (i) expects the network to host general-purpose softwarized network functions and (ii) encourages the packet payload to be modified. Yet, today’s networks are designed to focus on packet forwarding functions, and packet payloads should not be touched in the forwarding path, under the current end-to-end transport mechanisms. This dissertation presents fullstack in-network computing solutions, jointly designed from network and computing perspectives to accelerate data analysis applications, specifically for acoustic data analysis. In the computing domain, two design paradigms of computational logic, namely progressive computing and traffic filtering, are proposed in this dissertation for data reconstruction and feature extraction tasks. Two widely used practical use cases, Blind Source Separation (BSS) and anomaly detection, are selected to demonstrate the design of computing modules for data reconstruction and feature extraction tasks in the in-network computing scheme, respectively. Following these two design paradigms of progressive computing and traffic filtering, this dissertation designs two computing modules: progressive ICA (pICA) and You only hear once (Yoho) for BSS and anomaly detection, respectively. These lightweight computing modules can cooperatively perform computational tasks along the forwarding path. In this way, computational virtual functions can be introduced into the network, addressing the first challenge mentioned above, namely that the network should be able to host general-purpose softwarized network functions. In this dissertation, quantitative simulations have shown that the computing time of pICA and Yoho in in-network computing scenarios is significantly reduced, since pICA and Yoho are performed, simultaneously with the data forwarding. At the same time, pICA guarantees the same computing accuracy, and Yoho’s computing accuracy is improved. Furthermore, this dissertation proposes a stateful transport module in the network domain to support in-network computing under the end-to-end transport architecture. The stateful transport module extends the IP packet header, so that network packets carry message-related metadata (message-based packaging). Additionally, the forwarding layer of the network device is optimized to be able to process the packet payload based on the computational state (state-based transport component). The second challenge posed by in-network computing has been tackled by supporting the modification of packet payloads. The two computational modules mentioned above and the stateful transport module form the designed in-network computing solutions. By merging pICA and Yoho with the stateful transport module, respectively, two emulation systems, i.e., in-network pICA and in-network Yoho, have been implemented in the Communication Networks Emulator (ComNetsEmu). Through quantitative emulations, the experimental results showed that in-network pICA accelerates the overall service time of BSS by up to 32.18%. On the other hand, using in-network Yoho accelerates the overall service time of anomaly detection by a maximum of 30.51%. These are promising results for the design and actual realization of future communication networks.

Software Defined Networking (SDN) modernizes network control, offering streamlined management. However, its centralized structure makes it more vulnerable to distributed Denial of Service (DDoS) attacks, posing serious threats to network stability. This thesis explores the development of a DDoS attack prevention technique in SDN environments using the Ryu controller application. The research aims to address the vulnerabilities in SDN, particularly focusing on flooding and Internet Protocol (IP) spoofing attacks, which are a significant threat to network security. The study employs an experimental approach, utilizing tools like Mininet-VM (VirtualMachine), Oracle VM VirtualBox, and hping3 to simulate a virtual SDN environment and conduct DDoS attack scenarios. Key methodologies include packet sniffing and rule-based detection by integrating Snort IDS (Intrusion Detection System), which is critical for identifying and mitigating such attacks. The experiments demonstrate the effectiveness of the proposed prevention technique, highlighting the importance of proper configuration and integration of network security tools in SDN. This work contributes to enhancing the resilience of SDN architectures against DDoS attacks, offering insights into future developments in network security.

Radio-frequency (RF) fingerprinting is a process that uses the minute inconsistencies among manufactured radio transmitters to identify wireless devices. Coupled with location fingerprinting, which is a machine learning technique to locate devices based on their radio signals, it can uniquely identify and locate both trusted and rogue wireless devices transmitting over the air. This can have wide-ranging applications for the Internet of Things, security, and networking fields. To contribute to this effort, this research first builds a software-defined radio (SDR) testbed to collect an RF dataset over LTE and WiFi channels. The developed testbed consists of both hardware which are receivers with multiple antennas and software which performs signal preprocessing. Several features that can be used for RF device fingerprinting and location fingerprinting, including received signal strength indicator and channel state information, are also extracted from the signals. With the developed dataset, several data-driven machine learning algorithms have been implemented and tested for fingerprinting performance evaluation. Overall, experimental results show promising performance with a radio fingerprinting accuracy above 90\% and device localization within 1.10 meters.