优化风险缓解:基于仿真的智能城市环境中假物联网客户端检测模型

IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Sustainable Computing-Informatics & Systems Pub Date : 2024-07-19 DOI:10.1016/j.suscom.2024.101019
Mahmoud AlJamal , Ala Mughaid , Bashar Al shboul , Hani Bani-Salameh , Shadi Alzubi , Laith Abualigah
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引用次数: 0

摘要

智能城市代表着城市发展的未来,其特点是物联网(IoT)的复杂整合。在这种整合中,从交通管理到垃圾处理,一切都由相互连接的数字化管理系统来管理。这种城市的前景固然诱人,但也存在挑战。这种数字互联领域的一个重大问题是引入假冒客户。这些伪装成合法系统组件的实体可以实施一系列网络攻击。本研究利用 Netsim 程序设计了一个详细的智能城市模拟模型,重点研究假客户问题。在这个模拟环境中,多个部门与众多客户合作,以优化性能、舒适度和节能。假客户看似真实,实则心怀恶意,他们被引入模拟环境,以应对现实世界中的挑战。模拟配置完成后,使用 Wireshark 捕获数据流并保存为 CSV 文件,以区分真实和虚假客户。我们将 MATLAB 机器学习技术应用于捕获的数据集,以应对这些虚假客户带来的威胁。我们对各种机器学习算法进行了测试,k-近邻(KNN)分类器的检测准确率高达 98 77%。具体来说,我们的方法在三次实验中将检测准确率提高了 4.66%,从 94.02% 提高到 98.68%,并将曲线下面积 (AUC) 提高了 0.49%,达到 99.81%。精确度和召回率也有大幅提高,精确度提高了 9.09%,从 88.77% 提高到 97.86%,召回率提高了 9.87%,从 89.23% 提高到 99.10%。综合分析凸显了预处理在提高整体性能方面的作用,与传统方法相比,预处理在检测智慧城市环境中的虚假物联网客户端方面表现出色。我们的研究为保护智慧城市引入了一个强大的模型,将复杂的检测技术与强大的防御功能融为一体。
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Optimizing risk mitigation: A simulation-based model for detecting fake IoT clients in smart city environments

Smart cities represent the future of urban evolution, characterized by the intricate integration of the Internet of Things (IoT). This integration sees everything, from traffic management to waste disposal, governed by interconnected and digitally managed systems. As fascinating as the promise of such cities is, they have its challenges. A significant concern in this digitally connected realm is the introduction of fake clients. These entities, masquerading as legitimate system components, can execute a range of cyber-attacks. This research focuses on the issue of fake clients by devising a detailed simulated smart city model utilizing the Netsim program. Within this simulated environment, multiple sectors collaborate with numerous clients to optimize performance, comfort, and energy conservation. Fake clients, who appear genuine but with malicious intentions, are introduced into this simulation to replicate the real-world challenge. After the simulation is configured, the data flows are captured using Wireshark and saved as a CSV file, differentiating between the real and fake clients. We applied MATLAB machine learning techniques to the captured data set to address the threat these fake clients posed. Various machine learning algorithms were tested, and the k-nearest neighbors (KNN) classifier showed a remarkable detection accuracy of 98 77%. Specifically, our method increased detection accuracy by 4.66%, from 94.02% to 98.68% over three experiments conducted, and enhanced the Area Under the Curve (AUC) by 0.49%, reaching 99.81%. Precision and recall also saw substantial gains, with precision improving by 9.09%, from 88.77% to 97.86%, and recall improving by 9.87%, from 89.23% to 99.10%. The comprehensive analysis underscores the role of preprocessing in enhancing the overall performance, highlighting its superior performance in detecting fake IoT clients in smart city environments compared to conventional approaches. Our research introduces a powerful model for protecting smart cities, merging sophisticated detection techniques with robust defenses.

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来源期刊
Sustainable Computing-Informatics & Systems
Sustainable Computing-Informatics & Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTUREC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
10.70
自引率
4.40%
发文量
142
期刊介绍: Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.
期刊最新文献
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