Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment

Harshit Shah, Dhruvil Shah, Nilesh Kumar Jadav, Rajesh Gupta (), Sudeep Tanwar (), Osama Alfarraj, Amr Tolba, Maria Simona Raboaca () and Verdes Marina
Additional contact information
Harshit Shah: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Dhruvil Shah: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Nilesh Kumar Jadav: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Rajesh Gupta: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Osama Alfarraj: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Amr Tolba: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Maria Simona Raboaca: Doctoral School, University Politehnica of Bucharest, Splaiul Independentei Street No. 313, 060042 Bucharest, Romania
Verdes Marina: Faculty of Civil Engineering and Building Services, Department of Building Services, Technical University of Gheorghe Asachi, 700050 Iasi, Romania

Mathematics, 2023, vol. 11, issue 2, 1-22

Abstract: The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve.

Keywords: blockchain; artificial intelligence; intrusion detection system; internet of things; malicious smart contract (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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