Reem Alkanhel¹, El-Sayed M. El-kenawy², Abdelaziz A. Abdelhamid^3,4, Abdelhameed Ibrahim⁵, Manal Abdullah Alohali⁶, Mostafa Abotaleb⁷, Doaa Sami Khafaga^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2677-2693, 2023, DOI:10.32604/cmc.2023.033273 - 31 October 2022

Abstract Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require… More >

Ramesh Krishnamoorthy^*, Kalimuthu Krishnan

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 403-418, 2021, DOI:10.32604/iasc.2021.018560 - 11 August 2021

Abstract Due to the rapid growth of embedded devices, the selection of System-on-Chip (SoC) has a stronger influence to enable hardware security in embedded system design. System-on-chip (SoC) devices consist of one or more CPUs through wide-ranging inbuilt peripherals for designing a system with less cost. The selection of SoC is more significant to determine the suitability for secured application development. The design space analysis of symmetric key approaches including rivest cipher (RC5), advanced encryption standard (AES), data encryption standard (DES), international data encryption algorithm (IDEA), elliptic curve cryptography (ECC), MX algorithm, and the secure hash… More >

Yangyang Li¹, Xue Wang², Weiwei Fang^2,*, Feng Xue², Hao Jin¹, Yi Zhang¹, Xianwei Li³

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 493-508, 2019, DOI:10.32604/cmc.2019.05178

Abstract With the recent proliferation of Internet-of-Things (IoT), enormous amount of data are produced by wireless sensors and connected devices at the edge of network. Conventional cloud computing raises serious concerns on communication latency, bandwidth cost, and data privacy. To address these issues, edge computing has been introduced as a new paradigm that allows computation and analysis to be performed in close proximity with data sources. In this paper, we study how to conduct regression analysis when the training samples are kept private at source devices. Specifically, we consider the lasso regression model that has been More >