Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms
More significant data are available thanks to the present Internet of Things (IoT) application trend, which can be accessed in the future using some platforms for data storage. An external storage space is required for practical purposes whenever a data storage platform is created. However, in the I...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/3/747 |
| _version_ | 1797624725059403776 |
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| author | Wadee Alhalabi Amal Al-Rasheed Hariprasath Manoharan Eatedal Alabdulkareem Mai Alduailij Mona Alduailij Shitharth Selvarajan |
| author_facet | Wadee Alhalabi Amal Al-Rasheed Hariprasath Manoharan Eatedal Alabdulkareem Mai Alduailij Mona Alduailij Shitharth Selvarajan |
| author_sort | Wadee Alhalabi |
| collection | DOAJ |
| description | More significant data are available thanks to the present Internet of Things (IoT) application trend, which can be accessed in the future using some platforms for data storage. An external storage space is required for practical purposes whenever a data storage platform is created. However, in the IoT, certain cutting-edge storage methods have been developed that compromise the security and privacy of data transfer processes. As a result, the suggested solution creates a standard mode of security operations for storing the data with little noise. One of the most distinctive findings in the suggested methodology is the incorporation of machine learning algorithms in the formulation of analytical representations. The aforementioned integration method ensures high-level quantitative measurements of data security and privacy. Due to the transmission of large amounts of data, users are now able to assess the reliability of data transfer channels and the duration of queuing times, where each user can separate the specific data that has to be transferred. The created system is put to the test in real time using the proper metrics, and it is found that machine learning techniques improve security more effectively. Additionally, for 98 percent of the scenarios defined, the accuracy for data security and privacy is maximized, and the predicted model outperforms the current method in all of them. |
| first_indexed | 2024-03-11T09:46:36Z |
| format | Article |
| id | doaj.art-042a3514d1de4c6b95e75a214c9730b4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:46:36Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-042a3514d1de4c6b95e75a214c9730b42023-11-16T16:30:55ZengMDPI AGElectronics2079-92922023-02-0112374710.3390/electronics12030747Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning AlgorithmsWadee Alhalabi0Amal Al-Rasheed1Hariprasath Manoharan2Eatedal Alabdulkareem3Mai Alduailij4Mona Alduailij5Shitharth Selvarajan6Computer Science Department, Virtual Reality Research Group, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Electronics and Communication Engineering, Panimalar Engineering College, Chennai 600069, IndiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science and Engineering, Kebri Dehar University, Kebri Dehar 250, EthiopiaMore significant data are available thanks to the present Internet of Things (IoT) application trend, which can be accessed in the future using some platforms for data storage. An external storage space is required for practical purposes whenever a data storage platform is created. However, in the IoT, certain cutting-edge storage methods have been developed that compromise the security and privacy of data transfer processes. As a result, the suggested solution creates a standard mode of security operations for storing the data with little noise. One of the most distinctive findings in the suggested methodology is the incorporation of machine learning algorithms in the formulation of analytical representations. The aforementioned integration method ensures high-level quantitative measurements of data security and privacy. Due to the transmission of large amounts of data, users are now able to assess the reliability of data transfer channels and the duration of queuing times, where each user can separate the specific data that has to be transferred. The created system is put to the test in real time using the proper metrics, and it is found that machine learning techniques improve security more effectively. Additionally, for 98 percent of the scenarios defined, the accuracy for data security and privacy is maximized, and the predicted model outperforms the current method in all of them.https://www.mdpi.com/2079-9292/12/3/747software defined networks (SDN)securityprivacyInternet of Things (IoT)machine learning |
| spellingShingle | Wadee Alhalabi Amal Al-Rasheed Hariprasath Manoharan Eatedal Alabdulkareem Mai Alduailij Mona Alduailij Shitharth Selvarajan Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms Electronics software defined networks (SDN) security privacy Internet of Things (IoT) machine learning |
| title | Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms |
| title_full | Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms |
| title_fullStr | Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms |
| title_full_unstemmed | Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms |
| title_short | Distinctive Measurement Scheme for Security and Privacy in Internet of Things Applications Using Machine Learning Algorithms |
| title_sort | distinctive measurement scheme for security and privacy in internet of things applications using machine learning algorithms |
| topic | software defined networks (SDN) security privacy Internet of Things (IoT) machine learning |
| url | https://www.mdpi.com/2079-9292/12/3/747 |
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