A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things

Abstract

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 thousand 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 and breach 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 IoT networks that makes use of semantic information about IoT devices known as context information sensed from the deployed environment 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. We demonstrate the security of our proposed scheme and its resilience to secure clone node attack detection by conducting a comprehensive security analysis of our proposed scheme. The performance analysis and experimental results compared with existing studies suggest that our proposed scheme provides a high degree of detection accuracy with minimal detection time and significantly reduces the computation,

Towards a Formal Modelling, Analysis, and Verification of a Clone Node Attack Detection Scheme in the Internet of Things

“…This section provides an overview of our existing proposed clone node attack detection scheme [16], including the background description, methodology with the network components and proposed algorithms, to assist readers in understanding the proposed scheme's procedure and helping with the system modelling, analysis and verification processes.…”

“…Some other methods for finding the cloned node attacks include finding network witnesses [6], measuring the signal strength of devices [9], randomly picking secret keys [10], calculating trust values [11,12], managing distributed trust [13], localization algorithms [14] and multi-dimensional scaling [15]. A detailed analysis of existing schemes based on wireless sensor networks, IoT, and mobile ad-hoc networks for detecting clone node attacks is summarised here [16].…”

“…Additionally, another common trend observed in existing clone node attack detection schemes is that they provide a high level of abstraction for system architectures and evaluate schemes solely on well-known performance parameters such as detection rate, detection time, and various system overhead complexities such as computation, communication, and storage, without providing any modelling and verification of their works to verify the correctness and robustness. In this work, we conducted formal modelling, analysis and verification of our existing proposed clone node attack detection scheme [16] to provide the fine-grained abstraction level and to ensure that our proposed scheme does not contain any problematic scenarios or anomalies. Our proposed clone node attack detection scheme uses semantic information about IoT devices, known as context information, sensed from the deployed environment to locate them securely.…”