Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

Abstract

Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations' data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication-read file, second-level authentication-download file, and thirdlevel authentication-download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time.

Current Research Trends in IoT Security: A Systematic Mapping Study

“…e advanced encryption standard (AES) and error-correcting codes (ECC) are mainly used as basic lightweight encryption elements. Various studies have been aimed to optimize lightweight encryption while balancing security and performance management [69][70][71][72][73][74][75][76].…”

“…Lightweight encryption with low processing time and low power consumption is required to protect and secure data transmissions of resource-constrained IoT devices. Block encryption, such as AES and S-box, Galois Counter Mode, and physical unclonable functions, are being utilized, evaluated, and proposed [70,72,73,[182][183][184][185][186][187][188].…”

Feistel Network Assisted Dynamic Keying based SPN Lightweight Encryption for IoT Security

“…Additionally, these approaches undergo significantly large delay as well thus making it unsuitable towards realistic IoT-ecosystem. Undeniably, existing IoT-security systems employed cryptosystem(s) whether as standalone architecture or multifactor authentication [35]. It eventually imposes computational overheads and complexity, thus making it inappropriate for IoT systems.…”

“…Table V presents the encryption time results by the proposed GFS-SSPN lightweight encryption system. Noticeably, we used (21)(22)(23) to estimate the encryption and decryption time (ms) [35]. To be noted, the sample sizes (in bits) considered in this study were varied (here, increasing order) from 1 to 8.…”

A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

“…The big data is not accepted if privacy and security are not addressed. The scalability (S. Atiewi et al, 2020) is another major issue when the conventional preservation technique is adapted in big data. In spite of several techniques developed for privacy preservation, most of them cannot efficiently preserve the privacy as they fail to handle different attacks (Antony & Antony, 2016).…”