Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications
As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5540296 |
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| author | Mohammad Kamrul Hasan Muhammad Shafiq Shayla Islam Bishwajeet Pandey Yousef A. Baker El-Ebiary Nazmus Shaker Nafi R. Ciro Rodriguez Doris Esenarro Vargas |
| author_facet | Mohammad Kamrul Hasan Muhammad Shafiq Shayla Islam Bishwajeet Pandey Yousef A. Baker El-Ebiary Nazmus Shaker Nafi R. Ciro Rodriguez Doris Esenarro Vargas |
| author_sort | Mohammad Kamrul Hasan |
| collection | DOAJ |
| description | As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications. |
| format | Article |
| id | doaj-art-2b3c13b79fe144f790ee4b4470bae675 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2b3c13b79fe144f790ee4b4470bae6752025-02-03T01:05:27ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55402965540296Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things ApplicationsMohammad Kamrul Hasan0Muhammad Shafiq1Shayla Islam2Bishwajeet Pandey3Yousef A. Baker El-Ebiary4Nazmus Shaker Nafi5R. Ciro Rodriguez6Doris Esenarro Vargas7Center form Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, MalaysiaCyberspace Institute of Advanced Technology, Guanghzou University, Gaungzhou, ChinaInstitute of Computer Science and Digital Innovation, UCSI University, 56000 Kuala Lumpur, MalaysiaDepartment of Computer Science and Engineering, Birla Institute of Applied Science, Bhimtal, IndiaFaculty of Informatics and Computing, University Sultan Zainal Abidin (UniSZA), Kuala Terengganu, MalaysiaSchool of IT and Telecommunication Engineering, Melbourne Institute of Technology, Melbourne, AustraliaSchool of Software Engineering, National University Mayor de San Marcos, Lima, PeruUniversidad Nacional Federico Villarreal UNFV(INERN), Lima, PeruAs the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.http://dx.doi.org/10.1155/2021/5540296 |
| spellingShingle | Mohammad Kamrul Hasan Muhammad Shafiq Shayla Islam Bishwajeet Pandey Yousef A. Baker El-Ebiary Nazmus Shaker Nafi R. Ciro Rodriguez Doris Esenarro Vargas Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications Complexity |
| title | Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications |
| title_full | Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications |
| title_fullStr | Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications |
| title_full_unstemmed | Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications |
| title_short | Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications |
| title_sort | lightweight cryptographic algorithms for guessing attack protection in complex internet of things applications |
| url | http://dx.doi.org/10.1155/2021/5540296 |
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