Mahmoud AlJamal , Ala Mughaid , Bashar Al shboul , Hani Bani-Salameh , Shadi Alzubi , Laith Abualigah
{"title":"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","authors":"Mahmoud AlJamal , Ala Mughaid , Bashar Al shboul , Hani Bani-Salameh , Shadi Alzubi , Laith Abualigah","doi":"10.1016/j.suscom.2024.101019","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 101019"},"PeriodicalIF":3.8000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing risk mitigation: A simulation-based model for detecting fake IoT clients in smart city environments\",\"authors\":\"Mahmoud AlJamal , Ala Mughaid , Bashar Al shboul , Hani Bani-Salameh , Shadi Alzubi , Laith Abualigah\",\"doi\":\"10.1016/j.suscom.2024.101019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":48686,\"journal\":{\"name\":\"Sustainable Computing-Informatics & Systems\",\"volume\":\"43 \",\"pages\":\"Article 101019\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Computing-Informatics & Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210537924000647\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Computing-Informatics & Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210537924000647","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
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.
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.