The proceedings contain 53 papers. The topics discussed include: intelligent control system of chemical process based on internet of things;privacy-preserving key distribution protocol based on PUF for vehicle-to-grid...
ISBN:
(纸本)9781510653252
The proceedings contain 53 papers. The topics discussed include: intelligent control system of chemical process based on internet of things;privacy-preserving key distribution protocol based on PUF for vehicle-to-grid systems;research on fusion localization method based on roadside camera;a remote access and control scheme for smart home based on blockchain;survey on challenges of federated learning in edge computing scenarios;research on catenary condition maintenance strategy based on multi-objective optimization;research on equipment reliability evaluation method based on information fusion;condition evaluation of energized test devices based on multi-source heterogeneous data;the analysis of artificial intelligence technology: based on neural network;a study on the method of measuring the metaverse based on intelligent science;and construction of visual data scheduling and retrieval model for customer service data analysis platform.
The proceedings contain 73 papers. The topics discussed include: research on fire risk prediction method based on multi-source data perception;research on knowledge graph and GAN based data expansion methods;SCCGCN: a...
ISBN:
(纸本)9798400710353
The proceedings contain 73 papers. The topics discussed include: research on fire risk prediction method based on multi-source data perception;research on knowledge graph and GAN based data expansion methods;SCCGCN: a skip-connection coupled graph convolutional network with dynamic fusion attention mechanism for traffic flow prediction;transfer learning and CNN-based framework for intrusion detection in highway internet toll systems;secure computation offloading and resource allocation for ultra-dense multi-access mobile edge computing networks;a clustering algorithm for connectivity in cognitive vehicular network with digital twin;distribution network load forecasting based on deep learning;and construction of a news event influence assessment model based on machinelearning.
The proceedings contain 67 papers. The topics discussed include: optimization of transmission line information management system based on internet of things;application of internet of things in smart environmental pro...
ISBN:
(纸本)9781510664913
The proceedings contain 67 papers. The topics discussed include: optimization of transmission line information management system based on internet of things;application of internet of things in smart environmental protection in the context of big data;research on risk analysis method of industrial fieldbus based on data analysis;research on tourist volume monitoring system of smart scenic spot;distance education management system for high-tech agricultural talents based on cloud computing;variable screening with binary quantum behavior particle swarm optimization;method for inland water body extraction fused atrous spatial pyramid pooling;personalized recommendation system of modern science and technology resources based on hybrid filtering;research on a new electric power market trading strategy based on power big data;and research on location optimization in third-party logistics management system.
The proceedings contain 59 papers. The topics discussed include: high resolution underwater object detection network based on multi scale attention feature fusion;research on motion state detection based on multi-moda...
ISBN:
(纸本)9781510671805
The proceedings contain 59 papers. The topics discussed include: high resolution underwater object detection network based on multi scale attention feature fusion;research on motion state detection based on multi-modal analysis;a novel hot washing monitoring and management system of oil wells based on the internet of things;a new adversarial attack method based on Shapelet applied to traffic flow prediction model based on GCN;research on detection of insulator damage based on improved YOLOv4-tiny network;betweenness centrality approximation in large networks using shortest paths approximation and adaptive sampling;and unsupervised hypergraph convolutional clustering networks.
Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility support services and applications with highly variable requirements in areas such as the next-generation 5G/6G m...
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Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial internet of things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machinelearning, providing intra-slice and architectural operation security for the Slicing Future internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated-learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving average accuracy of approximately 95.60% in the network slicing architecture and 99.99% for the deployed slice - intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.
Button mushrooms are consumed worldwide, and they are beneficial to health (Adams LS et al. 2008). Farming of button mushrooms poses some difficulties to farmers all around the world. "Bacterial blotch, ‘cobwebs...
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This paper proposes a course intelligent recommendation system based on machinelearning algorithm to achieve more accurate personalized recommendation. The system uses deep learning model to design an optimized recom...
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Nitrate (NO3-) concentrations in aquifers constitute a global problem affecting environmental integrity and public health. Unfortunately, deploying hardware sensors specifically for NO3- measurements can be expensive,...
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Nitrate (NO3-) concentrations in aquifers constitute a global problem affecting environmental integrity and public health. Unfortunately, deploying hardware sensors specifically for NO3- measurements can be expensive, thereby, limiting scalability. This research explores the integration of soft sensors with data streams through an use case to predict nitrate NO3- levels in real time. To achieve this objective, a methodology based on Kafka-ML is proposed, a framework designed to manage the pipeline of machinelearning models using data streams. The study evaluates the effectiveness of this methodology by applying it to a real-world scenario, including the integration of low-cost sensor devices. Additionally, Kafka-ML is extended by integrating MQTT and other IoT data protocols. The methodology benefits include rapid development, enhanced control, and visualisation of soft sensors. By seamlessly integrating IoT and data analytics, the approach promotes the adoption of cost-effective solutions for managing NO3- pollution and improving sustainable water resource monitoring.
internet of things (IoT) devices offer convenience through web interfaces, web VPNs, and other web-based services, all relying on the HTTP protocol. However, these externally exposed HTTP services present significant ...
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internet of things (IoT) devices offer convenience through web interfaces, web VPNs, and other web-based services, all relying on the HTTP protocol. However, these externally exposed HTTP services present significant security risks. Although fuzzing has shown some effectiveness in identifying vulnerabilities in IoT HTTP services, most state-of-the-art tools still rely on random mutation strategies, leading to difficulties in accurately understanding the HTTP protocol's structure and generating many invalid test cases. Furthermore, These fuzzers rely on a limited set of initial seeds for testing. While this approach initiates testing, the limited number and diversity of seeds hinder comprehensive coverage of complex scenarios in IoT HTTP services. In this paper, we investigate and find that large language models (LLMs) excel in parsing HTTP protocol data and analyzing code logic. Based on these findings, we propose a novel LLM-guided IoT HTTP fuzzing method, ChatHTTPFuzz, which automatically parses protocol fields and analyzes service code logic to generate protocol-compliant test cases. Specifically, we use LLMs to label fields in HTTP protocol data, creating seed templates. Second, The LLM analyzes service code to guide the generation of additional packets aligned with the code logic, enriching the seed templates and their field values. Finally, we design an enhanced Thompson sampling algorithm based on the exploration balance factor and mutation potential factor to schedule seed templates. We evaluate ChatHTTPFuzz on 16 different real-world IoT devices. It finds more vulnerabilities than SNIPUZZ, BOOFUZZ, and MUTINY. ChatHTTPFuzz has discovered 116 vulnerabilities, of which 70 are unique, and 23 have been assigned CVEs.
With the rapid development of internet technology, the dissemination of malicious documents has become increasingly rampant, and traditional detection methods for malicious documents have failed to meet the current co...
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