Spoofed Networks: Exploitation of GNSS Security Vulnerability in 4G and 5G Mobile Networks

Includes supplementary materialFifth Generation New Radio (5G NR) represents a shift in mobile telephony whereby the network architecture runs containerized software on commodity hardware. In preparation for this transition, numerous 4G Long Term Evolution software stacks have been developed to test the containerization of core network functions and the interfaces with radio access network protocols. In this thesis, one such stack, developed by the OpenAirInterface Software Alliance, was used to create a low-cost, simplified mobile network compatible with the Naval Operational Architecture. Commercial off-the-shelf user equipment was then connected to the network to demonstrate how a buffer overflow vulnerability found in Qualcomm Global Navigation Satellite System chipsets and identified as CVE-2019-2254 can be leveraged to enable a spoofed network attack. The research also yielded an extension of the attack method to 5G NR networks.http://archive.org/details/aplaceholderreco1094567451Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Network-aware video streaming for future media Internet

272 p

ADVANCED RADIO ACCESS NETWORK FEATURING FLEXIBLE PER-UE SERVICE PROVISIONING AND COLLABORATIVE MOBILE EDGE COMPUTING

Enriched by numerous technological advances, radio access networks (RANs) in the fifth mobile networks generation (5G)-and-beyond strive to meet the goals of both mobile network operators (MNOs) and end-users. While MNOs seek efficiency, resiliency, reliability and flexibility of their networks, end-users are more concerned with the variety and quality of the provided, state-of-the-art, reasonably priced services. This has resulted in a complex, multi-tier, and heterogeneous RAN architecture that is severely challenged to achieve and maintain a strict reliability requirement of seven-nines (i.e., 99.99999% network up-time) and to meet ultra-reliable, low latency communications (URLLC) requirements with a latency upper bound of 1 ms end-to-end roundtrip time. Based on the flexible function split concept and data-plane programmability, this dissertation makes several key contributions to the body of knowledge on advanced, service-oriented RANs in two key core components. The first core component pertains to improving fronthaul efficiency, resiliency, flexibility, and latency performance with a cross-layer integration of Analog-Option-9 function split in the flexible fronthaul paradigm. Within the folds of that, the novel cross-layer digital-analog integration is experimentally investigated to pave the way for promising analog technologies to find their niche in 5G-and-beyond. The second core component is related to the design of lightweight, fronthaul-positioned multi-access edge computing (MEC) units to host Cooperative-URLLC applications at the edge of the fronthaul. Hence, from the vertical perspective, the dissertation provides solutions to support general URLLC applications and the Cooperative-URLLC variation by shrinking and eliminating latency sources at the Top-of-RAN and Low-RAN segments of advanced RANs.Ph.D