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检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是111-120 订阅

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QuadraCode AI: Smart Contract Vulnerability Detection with Multimodal Representation 33

QuadraCode AI: Smart Contract Vulnerability Detection with M...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Upadhya, Jiblal Upadhyay, Kritagya Sainju, Arpan Poudel, Samir Hasan, Md Nahid Poudel, Khem Ranganathan, Jaishree Middle Tennessee State Univ Dept Comp Sci Murfreesboro TN 37132 USA Middle Tennessee State Univ Dept Computat & Data Sci Murfreesboro TN 37132 USA

ISBN: (纸本)9798350348439;9798350384611

In recent years, Smart Contracts have gained in popularity, facilitating billions of US Dollars in daily transactions. However, the recent increase in smart contract vulnerabilities threatens to undermine trust in the technology. The study aims to detect and address potential vulnerabilities in smart contracts in blockchain technology through a comprehensive analysis of four principal modalities: Solidity source code, byte-code, opcode, and intermediate representations. This proactive identification of vulnerabilities can contribute to bolstering the security and dependability of blockchain-based systems. In this paper, we propose a novel multimodal Transformer architecture named QuadraCode AI, utilizing these four distinct modalities. Unlike traditional unimodal analysis, multimodal analysis can provide a more holistic understanding of both the semantic and syntactical contexts of smart contracts to identify underlying vulnerabilities. By employing advanced data fusion techniques such as cross-attention and concatenations across 12 different multimodal frameworks, our approach enhances the detection capabilities beyond traditional unimodal approach. Notably, the framework that integrates opcode with bytecode achieves an impressive average F score of 86%, demonstrating the effectiveness of our method.

关键词： Smart Contract Multimodality QuadraCode AI Security Concatenation Cross Attention Blockchain Deep Learning

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Edge Computing Network Intrusion Detection System in IoT Using Deep Learning 33

Edge Computing Network Intrusion Detection System in IoT Usi...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Hinojosa, Andres Majd, Nahid Ebrahimi Calif State Univ San Marcos Dept Comp Sci & Informat Syst San Marcos CA 92096 USA

ISBN: (纸本)9798350348439;9798350384611

A Network Intrusion Detection System (NIDS) is a technology to monitor the network traffic and detect and classify the possible cyber threats towards a computer network. In case it detects a potential cyberattack, it will alert the network prevent and defeat security system. Identifying the type of attack is invaluable for such system to block the malicious traffic or mitigate its impacts on the network. The Internet of Things (IoT) is a system of devices connected to the Internet, mainly for the purpose of acquiring data and transmitting that to the cloud, where the data will be processed, analyzed, and stored on the cloud and used by the user. In many cases, a group of IoT devices located at the same place form a network and send their data to the cloud through the network's gateway. As IoT technology is rapidly growing, it is crucial to develop more accurate and efficient intrusion detection and classification systems. In this paper, we propose efficient deep learning models that classify the network traffic to benign vs seven main types of IoT attacks with a focus on CICIoT2023 dataset at the edge of IoT network by leveraging edge computing. This research addresses the challenges of applying deep learning on intrusion detection datasets, including data imbalance and data distribution discrepancy using data preprocessing techniques, such as random under sampling, class weighting, and feature scaling. We also study the impact of different feature selection techniques on the efficiencies of our models. Our experimental results showed that our 1D-CNN model outperforms the state-of-the-art with 93.8% F1-score.

关键词： Network Intrusion Detection System Edge Computing Network security Network Attack Classification Deep Learning

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Leveraging Blockchain and Coded Computing for Secure Edge Collaborate Learning in Industrial IoT 33

Leveraging Blockchain and Coded Computing for Secure Edge Co...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Chen, Yuliang Lin, Xi Xu, Hansong Liao, Siyi Guo, Jin Zou, Chunming Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Key Lab Integrated Adm Technol Informat Shanghai Peoples R China China United Network Commun Co Ltd Shanghai Peoples R China Morgan State Univ Dept Comp Sci Baltimore MD 21239 USA Minist Publ Secur Res Inst 3 Shanghai Peoples R China

ISBN: (纸本)9798350348439;9798350384611

In recent years, the rapid development of the Industrial Internet of Things (IIoT) has enabled real-time communication and data sharing among devices, significantly enhancing industrial production efficiency and security. Furthermore, the introduction of edge learning allows models to be trained on edge industrial devices. However, with the continuous growth of industrial data, challenges such as resource optimization in edge environments, edge node motivation, and threats from malicious nodes are increasingly posing obstacles to the advancement of edge learning. In this paper, we propose Blockchain and Coded Computing based Secure Edge Learning (BCC-SEL). First, we introduce a coded edge learning framework with Lagrange Coded Computing (LCC) for resource-efficient use of idle nodes during training. Based on the blockchain, we further propose an incentive mechanism to reward and punish the participating training clients. Finally, we guarantee the robustness of the framework using the detection method based on cosine-similarity. We provide theoretical proof that our approach effectively reduces the computational consumption of training nodes. In terms of experiments, our method effectively rewards honest nodes that participate in training and penalizes malicious nodes while guaranteeing accuracy.

关键词： Edge learning Industrial Internet of Things Blockchain Coded computing

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QATFP-YOLO: Optimizing Object Detection on Non-GPU Devices with YOLO Using Quantization-Aware Training and Filter Pruning 33

QATFP-YOLO: Optimizing Object Detection on Non-GPU Devices w...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Idama, Gift Guo, Yifan Yu, Wei Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)9798350348439;9798350384611

Object detection is significant in real-world applications, including self-driving cars, surveillance systems, and vision-enabled robotic systems, among others. Despite the success of benchmark deep learning-based approaches, like YOLO, achieving high detection accuracy, they are typically computationally intensive and require GPUs to achieve optimal performance, preventing them from being widely deployed on low-power end-user devices. Particularly, when deploying these models on non-GPU devices, their inference speed is significantly degraded due to the lack of GPU support. To this end, in this paper, we propose an optimized object detection model called QATFP-YOLO (Quantization-Aware Training and Filter Pruning on YOLO), aiming to enhance inference speed on non-GPU devices, which could be trained and inferred on local end-user devices without GPU support. To reduce the computation complexity, we propose two optimized training strategies based on our QATFP-YOLO model by considering: (i) model quantization technique that reduces the model size and memory usage without sacrificing accuracy and (ii) filter pruning technique that removes redundant parameters from the model, further reducing memory usage and inference time. By evaluating the performance on a real smartphone, we find our QATFP-YOLO model achieves exceptional inference speeds, reaching approximately 88 frames per second, notably surpassing traditional YOLO-Lite models by over fourfold.

关键词： Quantization Aware Training Filter Pruning Object Detection YOLO Edge Intelligence Internet of Things

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Networking Experiments in the Keio Future Photonic Network Open Laboratory with Field-installed Hollow-Core Optical Fiber Cables 33

Networking Experiments in the Keio Future Photonic Network O...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Okamoto, Satoru Keio Univ Keio Frontier Res & Educ Collaborat Sq Shin Kawas Kawasaki Kanagawa Japan

ISBN: (纸本)9798350348439;9798350384611

The Keio Future Photonic Network Open Laboratory was opened in Apr. 2023 as a testing field for future Beyond 5G networking technology. This paper presents a proof-of-concept system for the new multi-access edge computing technology "AMec" using OpenLab's networking equipment, and actual application verification experiments using newly installed Hollow-Core optical fiber cables at OpenLab.

关键词： beyond 5G low latency multi-access edge computing hollow-core fiber passive optical network private 5G

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Privacy-Preserving Group Closeness Maximization 33

Privacy-Preserving Group Closeness Maximization

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Zhao, Zihao Cao, Sijia Zhang, Hanlin Lin, Jie Kong, Fanyu Yu, Leyun Qingdao Univ Coll Comp Sci Qingdao Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China JIC IOT CO LTD Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798350348439;9798350384611

This study explores the metric of group closeness centrality within the framework of social networks, a departure from the traditional analysis focused solely on the significance of individual nodes. Given the intricate dynamics observed in networks governed by various stakeholders, we introduce a framework that preserves privacy through the application of a greedy algorithm. This approach is designed to evaluate the collective influence of groups while ensuring the confidentiality of individual data. Furthermore, we employ Oblivious Random Access Memory (ORAM) [1] within cloud servers to conceal access patterns, thereby enhancing data privacy. Through comprehensive experimentation across three real-world social network datasets within the MP-SPDZ framework [2], dedicated to secure multi-party computation, we demonstrate the efficiency of our proposed methods.

关键词： privacy-preserving secure computation social network analysis group closeness centrality cloud computing

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DDD: A DNS-based DDoS Defense Scheme Using Puzzles 33

DDD: A DNS-based DDoS Defense Scheme Using Puzzles

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Lee, Hyeonmin Kang, Taehyun Yang, Sukhun Jun, Jinyong Kwon, Taekyoung Univ Virginia Charlottesville VA USA Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350348439;9798350384611

Distributed Denial-of-Service (DDoS) attacks have remained a significant threat to the Internet for years. One strategy for mitigating these attacks involves requiring clients to solve cryptographic puzzles to control the rate of incoming traffic to a target server. For such a puzzle-based DDoS defense mechanism to be effective, it necessitates robust methods for both distributing puzzles to clients and adjusting puzzle difficulty. In this paper, we introduce a puzzle-based DDoS defense mechanism, DDD, which utilizes the Domain Name System (DNS) for distributing puzzles to clients. The target server disseminates its puzzles by publishing them as a DNS record through its authoritative name server, distributing puzzles to clients via their DNS resolvers. Our design incorporates a monitoring server that continuously monitors incoming traffic to the target and dynamically adjusts puzzle difficulty based on the traffic originating from each Autonomous System (AS). This enables AS-specific puzzle difficulty customization, and consequently, traffic control. We have implemented our design into the Linux kernel and showcased its effectiveness in traffic control through prototype-based and controlled experiments.

关键词： Distributed Denial-of-Service Domain Name System Client Puzzle

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On the Impact of Overcoming Wake-up Radio Limitations on the Performance of Energy Aware Routing in Wireless Sensor networks 33

On the Impact of Overcoming Wake-up Radio Limitations on the...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Sheshashayee, Abhimanyu, V Petrioli, Chiara Basagni, Stefano Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Univ Roma La Sapienza Dept Comp Control & Management Engn Rome Italy

ISBN: (纸本)9798350348439;9798350384611

The integration of Wireless Sensor networks (WSNs) with the Internet of Things (IoT) has significantly broadened the scope of interconnected devices, offering novel solutions and enhancing capabilities in monitoring and control across various sectors. Despite remarkable advancements, reliance on battery-powered wireless devices introduces significant challenges, primarily due to energy constraints that limit the operational lifespan of these networks. This paper addresses these challenges by exploring the efficacy of Wake-up Radio (WuR) technology as a means to enhance energy efficiency. WuR technology allows nodes to remain dormant until communication is necessary, thereby extending the network lifetime without compromising performance. However, limitations such as reduced communication range and data transmission rates pose obstacles to the full realization of WuR potential. Through simulation-based experiments, this study evaluates the performance of a novel protocol, Simple Energy Aware Routing (SEAR), under various WuR configurations, using the GreenCastalia simulator. Our findings demonstrate how optimizing WuR parameters can significantly impact key network performance metrics, suggesting pathways for future WuR technology development to achieve optimal WSN performance within the IoT paradigm. The insights provided aim to inform ongoing research efforts, contributing to the evolution of WSNs as a foundational element of the IoT infrastructure.

关键词： Wireless sensor networks

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Guide to Disaster-Resilient Communication networks 1st ed. 2020

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丛书名： computer communications and networks

2020年

作者： Jacek Rak David Hutchison

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Towards Production Deployment of a SDX Control Framework 31

Towards Production Deployment of a SDX Control Framework

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31st International Conference on computer communications and networks (ICCCN)

作者： Cevik, Mert Stealey, Michael Wang, Cong Bezerra, Jeronimo Ibarra, Julio Chergarova, Vasilka Morgan, Heidi Xin, Yufeng Univ North Carolina Chapel Hill RENCI Chapel Hill NC 27599 USA Florida Int Univ CIARA Miami FL USA Univ Southern Calif ISI Marina Del Rey CA USA

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

Developing a distributed controller system for production software defined networks (SDN) requires substantial engineering effort to satisfy the stringent performance, adaptability, availability and security requirements that are well beyond the basic function development. The softwarization nature of SDN provides the opportunity to leverage the recent advancements in software engineering and system automation. In this paper, we present our recent work to develop and deploy a wide-area SDN control framework prototype towards production deployment and operation. We primarily focus on three critical areas: (1) enhancement of the software functions and the substrate virtualization configurations to fully support advanced connection services and fault tolerance in both data plane and control plane, (2) a high-fidelity testing pipeline consisting of unit tests, emulation, and a testbed, and (3) a continuous integration and continuous deployment (CI/CD) pipeline. Our experience proved that the presented environment and process greatly increased the software quality and development efficiency, which would ultimately lead to a reliable and continuous deployment and automation of the targeted production SDN network.

关键词： Pipelines Production Software quality Software reliability Security Software defined networking Virtualization

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