The rapidly expanding nature of the Internet of Things (IoT) networks is beginning to attract interest across a range of applications, including smart homes, smart transportation, smart health, and industrial contexts such as smart robotics. This cutting-edge technology enables individuals to track and control their integrated environment in real-time and remotely via a thousands of IoT devices comprised of sensors and actuators that actively participate in sensing, processing, storing and sharing information. Nonetheless, IoT devices are frequently deployed in hostile environments, wherein adversaries attempt to capture them in order to seize control of the entire network. One such example of potentially malicious behaviour is the cloning of IoT devices, in which an attacker can physically capture the devices, obtain some sensitive information, duplicate the devices, and intelligently deploy them in desired locations to conduct various insider attacks. A device cloning attack on IoT networks is a significant security concern since it allows for selective forwarding, sink-hole, black-hole, and warm-hole attacks. To address this issue, this paper provides an efficient scheme for detecting clone node attack on mobile IoT networks that uses semantic information of IoT devices known as context information to locate them securely. We design the location proof mechanism by combining location proofs and batch verification of the extended elliptic curve digital signature technique (ECDSA*) to accelerate the verification process at selected trusted nodes. Furthermore, we present a model for selecting trustworthy IoT devices based on their profile capabilities, enabling them to be chosen from the other IoT devices for the location proof-verification procedure. Compared with existing studies, the performance analysis and experimental results suggest that our proposed scheme provides a high degree of detection accuracy with minimal detection time and significantly reduces the computation, communication, energy and storage overheads.

Item Type:	Refereed Article
Keywords:	Internet of Things, clone node attack, clone detection, replica node detection, context-aware information, location proof
Research Division:	Information and Computing Sciences
Research Group:	Cybersecurity and privacy
Research Field:	System and network security
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Computer systems
UTAS Author:	Hameed, K (Mr Hameed Khizar)
UTAS Author:	Garg, S (Dr Saurabh Garg)
UTAS Author:	Amin, MB (Dr Muhammad Bilal Amin)
UTAS Author:	Kang, B (Professor Byeong Kang)
ID Code:	148027
Year Published:	2022
Deposited By:	Information and Communication Technology
Deposited On:	2021-11-30
Last Modified:	2022-04-27
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