2,044 research outputs found

    A Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

    ACiS: smart switches with application-level acceleration

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    Network performance has contributed fundamentally to the growth of supercomputing over the past decades. In parallel, High Performance Computing (HPC) peak performance has depended, first, on ever faster/denser CPUs, and then, just on increasing density alone. As operating frequency, and now feature size, have levelled off, two new approaches are becoming central to achieving higher net performance: configurability and integration. Configurability enables hardware to map to the application, as well as vice versa. Integration enables system components that have generally been single function-e.g., a network to transport data—to have additional functionality, e.g., also to operate on that data. More generally, integration enables compute-everywhere: not just in CPU and accelerator, but also in network and, more specifically, the communication switches. In this thesis, we propose four novel methods of enhancing HPC performance through Advanced Computing in the Switch (ACiS). More specifically, we propose various flexible and application-aware accelerators that can be embedded into or attached to existing communication switches to improve the performance and scalability of HPC and Machine Learning (ML) applications. We follow a modular design discipline through introducing composable plugins to successively add ACiS capabilities. In the first work, we propose an inline accelerator to communication switches for user-definable collective operations. MPI collective operations can often be performance killers in HPC applications; we seek to solve this bottleneck by offloading them to reconfigurable hardware within the switch itself. We also introduce a novel mechanism that enables the hardware to support MPI communicators of arbitrary shape and that is scalable to very large systems. In the second work, we propose a look-aside accelerator for communication switches that is capable of processing packets at line-rate. Functions requiring loops and states are addressed in this method. The proposed in-switch accelerator is based on a RISC-V compatible Coarse Grained Reconfigurable Arrays (CGRAs). To facilitate usability, we have developed a framework to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. In the third work, we extend ACiS to support fused collectives and the combining of collectives with map operations. We observe that there is an opportunity of fusing communication (collectives) with computation. Since the computation can vary for different applications, ACiS support should be programmable in this method. In the fourth work, we propose that switches with ACiS support can control and manage the execution of applications, i.e., that the switch be an active device with decision-making capabilities. Switches have a central view of the network; they can collect telemetry information and monitor application behavior and then use this information for control, decision-making, and coordination of nodes. We evaluate the feasibility of ACiS through extensive RTL-based simulation as well as deployment in an open-access cloud infrastructure. Using this simulation framework, when considering a Graph Convolutional Network (GCN) application as a case study, a speedup of on average 3.4x across five real-world datasets is achieved on 24 nodes compared to a CPU cluster without ACiS capabilities

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

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    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    Intrusion detection system in software-defined networks

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    Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSoftware-Defined Networking technologies represent a recent cutting-edge paradigm in network management, offering unprecedented flexibility and scalability. As the adoption of SDN continues to grow, so does the urgency of studying methods to enhance its security. It is the critical importance of understanding and fortifying SDN security, given its pivotal role in the modern digital ecosystem. With the ever-evolving threat landscape, research into innovative security measures is essential to ensure the integrity, confidentiality, and availability of network resources in this dynamic and transformative technology, ultimately safeguarding the reliability and functionality of our interconnected world. This research presents a novel approach to enhancing security in Software-Defined Networking through the development of an initial Intrusion Detection System. The IDS offers a scalable solution, facilitating the transmission and storage of network traffic with robust support for failure recovery across multiple nodes. Additionally, an innovative analysis module incorporates artificial intelligence (AI) to predict the nature of network traffic, effectively distinguishing between malicious and benign data. The system integrates a diverse range of technologies and tools, enabling the processing and analysis of network traffic data from PCAP files, thus contributing to the reinforcement of SDN security.As tecnologias de Redes Definidas por Software representam um paradigma recente na gestão de redes, oferecendo flexibilidade e escalabilidade sem precedentes. À medida que a adoção de soluções SDN continuam a crescer, também aumenta a urgência de estudar métodos para melhorar a sua segurança. É de extrema importância compreender e fortalecer a segurança das SDN, dado o seu papel fundamental no ecossistema digital moderno. Com o cenário de ameaças em constante evolução, a investigação de medidas de segurança inovadoras é essencial para garantir a integridade, a confidencialidade e a disponibilidade dos recursos da rede nesta tecnologia dinâmica e transformadora. Esta investigação apresenta uma nova abordagem para melhorar a segurança nas redes definidas por software através do desenvolvimento de um sistema inicial de deteção de intrusões. O IDS oferece uma solução escalável, facilitando a transmissão e o armazenamento do tráfego de rede com suporte robusto para recuperação de falhas em vários nós. Além disso, um módulo de análise inovador incorpora inteligência artificial (IA) para prever a natureza do tráfego de rede, distinguindo efetivamente entre dados maliciosos e benignos. O sistema integra uma gama diversificada de tecnologias e ferramentas, permitindo o processamento e a análise de dados de tráfego de rede a partir de ficheiros PCAP, contribuindo assim para o reforço da segurança SDN

    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Caching-based Multicast Message Authentication in Time-critical Industrial Control Systems

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    Attacks against industrial control systems (ICSs) often exploit the insufficiency of authentication mechanisms. Verifying whether the received messages are intact and issued by legitimate sources can prevent malicious data/command injection by illegitimate or compromised devices. However, the key challenge is to introduce message authentication for various ICS communication models, including multicast or broadcast, with a messaging rate that can be as high as thousands of messages per second, within very stringent latency constraints. For example, certain commands for protection in smart grids must be delivered within 2 milliseconds, ruling out public-key cryptography. This paper proposes two lightweight message authentication schemes, named CMA and its multicast variant CMMA, that perform precomputation and caching to authenticate future messages. With minimal precomputation and communication overhead, C(M)MA eliminates all cryptographic operations for the source after the message is given, and all expensive cryptographic operations for the destinations after the message is received. C(M)MA considers the urgency profile (or likelihood) of a set of future messages for even faster verification of the most time-critical (or likely) messages. We demonstrate the feasibility of C(M)MA in an ICS setting based on a substation automation system in smart grids.Comment: For viewing INFOCOM proceedings in IEEE Xplore see https://ieeexplore.ieee.org/abstract/document/979676

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Path Protection Switching in Information Centric Networks (ICN)

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    Since its formation, the Internet has experienced tremendous growth, constantly increasing traffic and new applications, including voice and video. However, it still keeps its original architecture drafted almost 40 years ago built on the end-to-end principle; this has proven to be problematic when there are failures as routing convergence is slow for unicast networks and even slower for multicast which has to rely upon slow multicast routing as no protection switching exists for multicast. This thesis investigates protection in an alternative approach for network communication, namely information centric networking (ICN) using the architecture proposed by the PSIRP/PURSUIT projects. This uses Bloom Filters to allow both unicast and multicast forwarding. However, the PSIRP/PURSUIT ICN approach did not investigate protection switching and this problem forms the main aim of this thesis. The work builds on the research by Grover and Stamatelakis who introduced the concept of pre-configured protection p-cycles in 2000 for optical networks and, with modification, applicable to unicast IP or packet networks. This thesis shows how the p-cycle concept can be directly applied to packet networks that use PSIRP/PURSUIT ICN and extends the approach to encompass both unicast and multicast protection switching. Furthermore, it shows how the chosen p-cycles can be optimised to reduce the redundancy overhead introduced by the protection mechanism. The work evaluates the approach from two aspects, the first is how the proposed approach compares to existing switching state and traffic in an MPLS multicast architecture. The second considers the redundancy overhead in three known network topologies for synthetic traffic matrices. The thesis is the first work to demonstrate the efficiency of Bloom filter based switching for multicast (and unicast) protection switching
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