Recently, there has been much interest in the resilience of complex networks to random failures and intentional attacks. The study of the network robustness is particularly important by several occasions. In one hand a higher degree of robustness to errors and attacks may be desired for maintaining the information flow in communication networks under attacks. On the other hand planning a very limited attack aimed at fragmenting a network by removal of minimum number of the most important nodes might have significant usage in drug design. Many real world networks were found to display scale free topology including WWW, the internet, social networks or regulatory gene and protein networks. In the recent studies it was shown that while these networks have a surprising error tolerance, their scale-free topology makes them fragile under intentional attack, leaving the scientists a challenge on how to improve the networks robustness against attacks. In this thesis, we studied the protein domain co-occurrence network of yeast which displays scale free topology generated with data from Biomart which links to Pfam database. Several networks obtained from protein domain co-occurrence network having exactly the same connectivity distribution were compared under attacks to investigate the assumption that the different networks with the same connectivity distribution do not need to have the same attack tolerances. In addition to this, we considered that the networks with the same connectivity distribution have higher attack tolerance as we organize the same resources in a better way. Then, we checked for the variations of attack tolerance of the networks with the same connectiviy distributions. Furthermore, we investigated whether there is an evolutionary mechanism for having networks with higher or lower attack tolerances for the same connectivity distribution. As a result of these investigations, the different networks with the same connectivity distribution do not have the same attack tolerances under attack. In addition to this, it was observed that the networks with the same connectivity distribution have higher attack tolerances as organizing the same resources in a better way which implies that there is an evolutionary mechanism for having networks with higher attack tolerance for the same connectivity distribution.

Named Data Networking (NDN) is a recently proposed new Internet architecture. By naming data instead of locations, it changes the very basic network service abstraction from "delivering packets to given destinations" to "retrieving data of given names." This fundamental change creates an abundance of new opportunities as well as many intellectual challenges in application development, network routing and forwarding, communication security and privacy. The focus of this dissertation is a unique feature introduced by NDN: its adaptive forwarding plane. Communication in NDN is done by exchanges of Interest and Data packets. Consumers send Interest packets to request desired Data, routers forward them based on data names, and producers answer with Data packets, which take the same path of Interests but in reverse direction. During this process, routers maintain state information of pending Interests. This state information, coupled with the symmetric exchange of Interest and Data, enables NDN routers to detect loops, observe data retrieval performance, and explore multiple forwarding paths, all at the forwarding plane. Since NDN is still in its early stage, however, none of these powerful features has been systematically designed, valuated, or explored. In this dissertation, we present a concrete design of NDN's forwarding plane to make the network resilient and efficient. First, we design the basic adaptation mechanism and evaluate its effectiveness in circumventing prefix hijack attacks. Second, we propose a novel NACK mechanism for fast failure detection and evaluate its benefits in handling network failures. We also show that a resilient forwarding plane makes routing more stable and more scalable. Third, we design a congestion control mechanism, Dynamic Interest Limiting, to adapt traffic rate in a hop-by-hop and multipath fashion, which is effective even with a large number of flows in a large network topology.

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Integrating Terrestrial and Non-Terrestrial Networks (NTNs) within Beyond-5G (B5G) and future 6G ecosystems represents a transformative advancement in achieving ubiquitous, resilient, and scalable communication services. NTNs, including Low Earth Orbit (LEO) satellites, Unmanned Aerial Vehicles (UAVs), and High Altitude Platform Systems (HAPS), extend traditional terrestrial networks by providing continuous connectivity in remote, underserved, and connection-critical scenarios such as disaster-hit regions and rural areas. This thesis deals with an end-to-end cloud-native framework that leverages cutting-edge technologies, including Multi-Access Edge Computing (MEC), Software Defined Networking (SDN), Network Function Virtualization (NFV), blockchain, and advanced AI/ML models, to enhance service availability, security, and Quality of Service (QoS) in 3D NTN environments. The research first explores the deployment of disaggregated Next-Generation Radio Access Networks (NGRANs) across terrestrial and non-terrestrial domains using a Kubernetes-based architecture. A Graph Neural Network (GNN) model is developed to monitor and manage these networks, detecting link failures and optimizing traffic routing paths between terrestrial and satellite components. The GNN model achieves an 85% accuracy in link failure detection and significantly reduces end-to-end delays in NTN deployments, highlighting the potential of AI-driven network management in enhancing overall network resilience. To address the challenge of dynamic resource management in NTNs, this thesis investigates the implementation of functional splits, such as F1 and E1 interfaces, between terrestrial control units (gNB-CU) and satellite-based distributed units (gNB-DU). The study employs Long Short-Term Memory (LSTM) neural networks to predict resource utilization, specifically CPU, memory, and bandwidth of satellite payloads. These predictive models enable proactive monitoring and resource allocation decisions, ensuring efficient use of limited computational resources and maintaining high levels of network performance. Security remains a critical concern in NTNs due to decentralized and open 5G satellite communications. A blockchain-based authentication framework is proposed to mitigate these risks, enhancing the security of data exchanges and remote firmware updates in LEO satellite constellations. Blockchain technology provides a decentralized, transparent, and immutable security framework, improving authentication efficiency and protecting against unauthorized access, though with trade-offs in network performance, such as increased latency and reduced throughput. This approach makes the hybrid B5G NTN network secure, reinforcing the integrity and confidentiality of communication channels, which is essential for emerging services and applications. Furthermore, this thesis comprehensively evaluates MEC-based experimental testbeds that demonstrate service resiliency in NTNs during terrestrial network outages. The MEC deployments allow seamless transitions to satellite access networks, ensuring service continuity and improving QoS. These testbeds showcase the capability of cloud native technologies in maintaining service availability, highlighting their critical role in resilient NTN networks. The findings of this thesis demonstrate that integrating cloud-native architectures, blockchain-based security mechanisms, and advanced AI/ML models significantly enhances the resilience, security, and resource efficiency of NTNs. These innovations pave the way for robust, adaptive, and secure communication systems, supporting the seamless deployment of critical B5G and 6G applications across diverse and challenging environments. This research provides valuable insights into designing and implementing resilient NTNs, setting the foundation for future advancements in global connectivity and intelligent network management.

A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal. Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis. First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis. Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed.

Among the many social structures that cause inequality, one of the most jarring is on the use of loopholes to both launder money and evade taxation. Such resources fuel the \"offshore finance\" industry, a multi-billion dollar sector catering to many of those needs. These run under the logic of \"Dark Networks\" avoiding detection and oversight as much as possible. While there are legitimate uses for offshore services, such as protecting assets from unlawful seizures, they are also a well documented pipeline for money stemming from ilegal activities. These constructs display a high amount of adaptiveness and resilience and the few studies done had to use incomplete information, mostly from local sources of criminal proceedings. This work is to analyzes the network of offshore accounts leaked under the Panama Papers report by the International Consortium of Investigative Journalists. This registers the activities of the Mossack Fonseca law firm in Panama, one of the largest in the world on the Offshore field. It spans over 50 years and provide us with one of the most complete overview thus far of how these activities are connected, the topology of such network and what it displays in resilience against attempts to target this scheme / Dentre as muitas estruturas sociais que causam desigualdades, uma das mais estarrecedoras é o uso de brechas para lavagem de dinheiro e evasão fiscal. Estes recursos sustentam a rede de finanças Offshore, uma industria multi-bilionária que oferece serviços para muitas dessas metas. Estas funcionam na lógicas das Redes Sombrias, evitando detecção e supervisão sempre que possível. Ainda que existam razões legitimas para o uso de serviços offshore, como a proteção de bens contra apropriação indébita, eles são um canal bem documentado para as receitas advindas de atividades ilegais . Este trabalho analisa a rede de contas offshore vazada sob a égide dos Panama Papers pelo Consorcio Internacional de Jornalistas Investigativos, que registrou a atividade da firma de advocacia Mossack Fonseca, sediada no Panama e uma das maiores do setor de Offshore. Com mais de 50 anos de registros, é ate o momento nosso panorama mais completo do padrão de conexão destas atividades, da topologia desta rede e do que ela demonstra de resiliência contra tentativas de desmontar esse esquema

Among the many social structures that cause inequality, one of the most jarring is on the use of loopholes to both launder money and evade taxation. Such resources fuel the \"offshore finance\" industry, a multi-billion dollar sector catering to many of those needs. These run under the logic of \"Dark Networks\" avoiding detection and oversight as much as possible. While there are legitimate uses for offshore services, such as protecting assets from unlawful seizures, they are also a well documented pipeline for money stemming from ilegal activities. These constructs display a high amount of adaptiveness and resilience and the few studies done had to use incomplete information, mostly from local sources of criminal proceedings. This work is to analyzes the network of offshore accounts leaked under the Panama Papers report by the International Consortium of Investigative Journalists. This registers the activities of the Mossack Fonseca law firm in Panama, one of the largest in the world on the Offshore field. It spans over 50 years and provide us with one of the most complete overview thus far of how these activities are connected, the topology of such network and what it displays in resilience against attempts to target this scheme / Dentre as muitas estruturas sociais que causam desigualdades, uma das mais estarrecedoras é o uso de brechas para lavagem de dinheiro e evasão fiscal. Estes recursos sustentam a rede de finanças Offshore, uma industria multi-bilionária que oferece serviços para muitas dessas metas. Estas funcionam na lógicas das Redes Sombrias, evitando detecção e supervisão sempre que possível. Ainda que existam razões legitimas para o uso de serviços offshore, como a proteção de bens contra apropriação indébita, eles são um canal bem documentado para as receitas advindas de atividades ilegais . Este trabalho analisa a rede de contas offshore vazada sob a égide dos Panama Papers pelo Consorcio Internacional de Jornalistas Investigativos, que registrou a atividade da firma de advocacia Mossack Fonseca, sediada no Panama e uma das maiores do setor de Offshore. Com mais de 50 anos de registros, é ate o momento nosso panorama mais completo do padrão de conexão destas atividades, da topologia desta rede e do que ela demonstra de resiliência contra tentativas de desmontar esse esquema

Critical infrastructure systems are essential for the continuous functionality of modern global societies. Some examples of these systems include electric energy, potable water, oil and gas, telecommunications, and the internet. Different topologies underline the structure of these networked systems. Each topology (i.e., physical layout) conditions the way in which networks transmit and distribute their flow. Also, their ability to absorb unforeseen natural or intentional disruptions depends on complex relations between network topology and optimal flow patterns. Most of the current research on large networks is focused on understanding their properties using statistical physics, or on developing advanced models to capture network dynamics. Despite these important research efforts, almost all studies concentrate on specific networks. This network-specific approach rules out a fundamental phenomenon that may jeopardize the performance predictions of current sophisticated models: network response is in general interdependent, and its performance is conditioned on the performance of additional interacting networks. Although there are recent conceptual advances in network interdependencies, current studies address the problem from a high-level point of view. For instance, they discuss the problem at the macro-level of interacting industries, or utilize economic input-output models to capture entire infrastructure interactions. This study approaches the problem of network interdependence from a more fundamental level. It focuses on network topology, flow patterns within the networks, and optimal interdependent system performance. This approach also allows for probabilistic response characterization of interdependent networked systems when subjected to disturbances of internal nature (e.g., aging, malfunctioning) or disruptions of external nature (e.g., coordinated attacks, seismic hazards). The methods proposed in this study can identify the role that each network element has in maintaining interdependent network connectivity and optimal flow. This information is used in the selection of effective pre-disaster mitigation and post-disaster recovery actions. Results of this research also provide guides for growth of interacting infrastructure networks and reveal new areas for research on interdependent dynamics. Finally, the algorithmic structure of the proposed methods suggests straightforward implementation of interdependent analysis in advanced computer software applications for multi-hazard loss estimation.

A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal. Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis. First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis. Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed.

In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving. / Doctor of Philosophy / In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving.