TY - JOUR
T1 - Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications
AU - Hasan, Mohammad Kamrul
AU - Shafiq, Muhammad
AU - Islam, Shayla
AU - Pandey, Bishwajeet
AU - Baker El-Ebiary, Yousef A.
AU - Nafi, Nazmus Shaker
AU - Ciro Rodriguez, R.
AU - Vargas, Doris Esenarro
N1 - Publisher Copyright:
© 2021 Mohammad Kamrul Hasan et al.
PY - 2021
Y1 - 2021
N2 - 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 devices 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 devices 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 networks 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.
AB - 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 devices 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 devices 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 networks 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.
UR - http://www.scopus.com/inward/record.url?scp=85104537246&partnerID=8YFLogxK
U2 - 10.1155/2021/5540296
DO - 10.1155/2021/5540296
M3 - Article
AN - SCOPUS:85104537246
SN - 1076-2787
VL - 2021
JO - Complexity
JF - Complexity
M1 - 5540296
ER -