Document Details
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Document Type |
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Thesis |
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Document Title |
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IoT Data Protection Using Hybrid Cryptographic algorithms حماية بيانات إنترنت الأشياء باستخدام خوارزميات التشفير الهجين |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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The Internet of Things (IoT) is a fast-growing technology that has modernized human lives and provided numerous benefits worldwide. The IoT connects many objects over the Internet to transmit information and perform tasks based on sensor information. This technology has become widely used in many fields, such as smart homes, smart cities, and medicine. With this revolution in IoT technology and the increase in demand for it, security concerns and data confidentiality have become important concerns for consumers of IoT applications. In particular, if IoT applications depend on the production of big data, keeping it secure is a significant challenge. The most important way to protect data from security threats is to store it in encrypted form. In this research, we will study three cases. In Case (1), we apply a proposed hybrid cryptography algorithm consisting of two types of encryption algorithms, the symmetric DES algorithm, and the asymmetric RSA algorithm. This approach is applied to Mhealth, a big IoT dataset, to protect it from unauthorized access during data storage. In Case (2), a data compression technology is applied before the hybrid cryptography algorithm. This reduces both the required storage space and the encryption and decryption times. Finally, in Case (3), we use a deep learning model, the auto-encoder model, to extract some critical and sensitive data features before applying the hybrid cryptography algorithm. The three approaches are compared by measuring their encoding time, decoding time, and throughput. We determine that Case (3) is the most efficient approach: it achieves 10% faster encryption and decryption than Case (2), which is in turn 49% more efficient than Case (1). |
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Supervisor |
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Dr. Khalid Ateatallah Alsubhi |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1444 AH
2023 AD |
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Added Date |
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Thursday, June 29, 2023 |
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Researchers
| مها رده الله الطلحي | Altalhi, Maha Ruddah | Researcher | Master | |
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