利用联合学习和拆分学习对智慧城市中的物联网网络攻击进行异常检测

Ishaani Priyadarshini
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摘要

物联网(IoT)设备在智能城市基础设施中的迅速普及,迫切要求采取强有力的网络安全措施。这些设备很容易受到各种网络攻击,从而危及城市系统的安全和功能。本研究提出了一种创新方法,用于识别智慧城市中物联网网络攻击导致的异常情况。所提出的方法利用了联合学习和拆分学习,解决了增强物联网网络安全和保护数据隐私的双重挑战。本研究使用来自智慧城市的真实数据集进行了大量实验。为了比较经典机器学习算法和深度学习模型在检测异常情况方面的性能,使用精度、召回率、F-1 分数、准确率和训练/部署时间来评估模型的有效性。研究结果表明,联合学习和拆分学习有可能在数据隐私问题和竞争性能之间取得平衡,为检测物联网网络攻击提供强大的解决方案。这项研究为正在进行的关于确保城市环境中物联网部署安全的讨论做出了贡献。它为可扩展、注重隐私的网络安全战略奠定了基础。研究结果强调了这些技术在强化智慧城市和促进物联网时代适应性和弹性网络安全措施发展方面的重要作用。
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Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning
The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and functionality of urban systems. This research presents an innovative approach to identifying anomalies caused by IoT cyberattacks in smart cities. The proposed method harnesses federated and split learning and addresses the dual challenge of enhancing IoT network security while preserving data privacy. This study conducts extensive experiments using authentic datasets from smart cities. To compare the performance of classical machine learning algorithms and deep learning models for detecting anomalies, model effectiveness is assessed using precision, recall, F-1 score, accuracy, and training/deployment time. The findings demonstrate that federated learning and split learning have the potential to balance data privacy concerns with competitive performance, providing robust solutions for detecting IoT cyberattacks. This study contributes to the ongoing discussion about securing IoT deployments in urban settings. It lays the groundwork for scalable and privacy-conscious cybersecurity strategies. The results underscore the vital role of these techniques in fortifying smart cities and promoting the development of adaptable and resilient cybersecurity measures in the IoT era.
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