Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks
In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/12/12/262 |
| _version_ | 1827575168035192832 |
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| author | Abdullah Ali Jawad Al-Abadi Mbarka Belhaj Mohamed Ahmed Fakhfakh |
| author_facet | Abdullah Ali Jawad Al-Abadi Mbarka Belhaj Mohamed Ahmed Fakhfakh |
| author_sort | Abdullah Ali Jawad Al-Abadi |
| collection | DOAJ |
| description | In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies. |
| first_indexed | 2024-03-08T20:52:22Z |
| format | Article |
| id | doaj.art-03c8b661436b41f2a84b7b511977c859 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-08T20:52:22Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-03c8b661436b41f2a84b7b511977c8592023-12-22T14:01:25ZengMDPI AGComputers2073-431X2023-12-01121226210.3390/computers12120262Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things NetworksAbdullah Ali Jawad Al-Abadi0Mbarka Belhaj Mohamed1Ahmed Fakhfakh2Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), University of Sfax, National School of Engineers of Sfax (ENIS), Sfax 3038, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), University of Sfax, National School of Engineers of Gabes (ENIG), Gabes 6029, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), University of Sfax, National School of Electronics and Telecommunications of Sfax (ENET’com), Sfax 1163, TunisiaIn recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies.https://www.mdpi.com/2073-431X/12/12/262Internet of Medical ThingsDDoS attacksMITM attacksmachine learningpreventionsimulation |
| spellingShingle | Abdullah Ali Jawad Al-Abadi Mbarka Belhaj Mohamed Ahmed Fakhfakh Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks Computers Internet of Medical Things DDoS attacks MITM attacks machine learning prevention simulation |
| title | Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks |
| title_full | Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks |
| title_fullStr | Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks |
| title_full_unstemmed | Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks |
| title_short | Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in Internet-of-Medical-Things Networks |
| title_sort | enhanced random forest classifier with k means clustering erf kmc for detecting and preventing distributed denial of service and man in the middle attacks in internet of medical things networks |
| topic | Internet of Medical Things DDoS attacks MITM attacks machine learning prevention simulation |
| url | https://www.mdpi.com/2073-431X/12/12/262 |
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