Incorporating smart technology into critical infrastructure (CI) and smart cities promises substantial efficiency improvements as networks of machines communicate and make rapid decisions autonomously. Yet the promise...
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ISBN:
(数字)9781665497268
ISBN:
(纸本)9781665497268
Incorporating smart technology into critical infrastructure (CI) and smart cities promises substantial efficiency improvements as networks of machines communicate and make rapid decisions autonomously. Yet the promise of greater efficiency that such cyber-physical systems (CPS) bring is tempered by increased fragility unless machine-to-machine (M2M) trust is enhanced, particularly in Internet of Things (IoT) networks. This work makes two contributions toward improving M2M trust. First, it proposes a multifaceted trust framework comprised of identity verification, experience, context, and recommendation scores to enable high-integrity M2M interactions. Second, this trust framework is implemented via an IoT-friendly distributed ledger on a physical testbed, where it is shown to identify and mitigate errors due to a compromised system component. This implementation mirrors real-world IoT systems in which resource-constrained endpoint devices pose trust score computation challenges and the number of devices raises scalability obstacles for information sharing among nodes.
Quantization replaces floating point arithmetic with integer arithmetic in deep neural networks, enabling more efficient on-device inference with less power and memory. However, it also brings in loss of generalizatio...
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ISBN:
(纸本)9783031651113;9783031651120
Quantization replaces floating point arithmetic with integer arithmetic in deep neural networks, enabling more efficient on-device inference with less power and memory. However, it also brings in loss of generalization and even potential errors to the models. In this work, we propose a parallelization technique for formally verifying the equivalence between quantized models and their original real-valued counterparts. In order to guarantee both soundness and completeness, mixed integer linear programming (MILP) is deployed as the baseline technique. Nevertheless, the incorporation of two networks as well as the mixture of integer and real number arithmetic make the problem much more challenging than verifying a single network, and thus using MILP alone is inadequate for the non-trivial cases. To tackle this, we design a distributed verification technique that can leverage hundreds of CPUs on high-performance computing clusters. We develop a two-tier parallel framework and propose property- and output-based partition strategies. Evaluated on perception networks quantized with PyTorch, our approach outperforms existing methods in successfully verifying many cases that are otherwise considered infeasible.
Opinion formation in social networks has a significant impact on the society. Consequently, accurate opinion formation forecasts that can be effectively controlled and handled are particularly desirable. Current opini...
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Wireless Mesh networks, has advantages over existing wireless mechanisms, and will be the mainstay of future-generation wireless networking. In this subject, a lot of difficult technological problems are still unresol...
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Current advancements in the 5G network slicing admit a huge number of users avail a variety of services from the 5G Core network (5GC). While this is impressive, it opens the doors to security threats that could harm ...
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ISBN:
(纸本)9781665477062
Current advancements in the 5G network slicing admit a huge number of users avail a variety of services from the 5G Core network (5GC). While this is impressive, it opens the doors to security threats that could harm the smooth functioning of the 5GC as well as the legitimate users availing its services. This paper presents SENTINEL - a novel self protecting 5GC in the control plane by leveraging the Self Organizing Network (SON) paradigm. SENTINEL is fabricated as an autonomous framework, which protects itself in the control plane operations of 5GC from distributed Denial of Service (DDoS) attack attempts by malicious users. Precisely, we build it as an Artificial Intelligence-based Hierarchical Temporal Memory (HTM) framework along with eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) based slice aggregator to aid in protecting the slice when the malicious users attempt a DDoS attack. While the attack is aimed at the 5GC control plane, the SENTINEL isolates the suspected malicious users with a sensitivity of '85.59%' from the 5GC. Thereby, it keeps the High Availability (HA) service for legitimate users intact, without incurring additional resources.
In formal epistemology, group knowledge is often modelled as the knowledge that the group would have if the agents shared all their individual knowledge. However, this interpretation does not account for relations bet...
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Now days, Internet of Things (IoTs) is the most promising area of research, for the problems, in order to create distributed and innovative solutions. An IoT connects large amount of heterogeneous devices in the netwo...
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In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model. This approa...
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ISBN:
(纸本)9781665497473
In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model. This approach aims to improve communication performance, especially for varying channel conditions, with the cost of high computational complexity for training and inference. Field-programmable gate arrays (FPGAs) have been shown to be a suitable platform for energy-efficient ANN implementation. However, the high number of operations and the large model size of ANNs limit the performance on the resource-constrained devices, which is critical for low-latency and high-throughput communication systems. To tackle his challenge, we propose a novel approach for efficient ANN-based demapping on FPGAs, which combines the adaptability of the AE with the efficiency of conventional demapping algorithms. After adaption to channel conditions, the channel characteristics, implicitly learned by the ANN, are extracted to enable the use of optimized conventional demapping algorithms for inference. We validate the hardware efficiency of our approach by providing FPGA implementation results and by comparing the communication performance to that of conventional systems. Our work opens a door for the practical application of ANN-based communication algorithms on FPGAs.
Recent advances in internet of vehicles (IoV) have highlighted the importance of vehicular networks in intelligent transportation systems (ITS). The rapid growth in the number of connected sensors and users requires a...
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