A Context-Aware Information-Based Clone Node Attack Detection Scheme in Internet of Things
This addresses security concerns for IoT applications like smart homes and industrial systems, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of clone node attacks in IoT networks by proposing a detection scheme that uses context information and a location proof mechanism with batch verification, achieving high detection accuracy and minimal overhead.
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. 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, and black-hole attacks. To address this issue, this paper provides an efficient scheme for detecting clone node attacks 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 a location proof mechanism by combining location proofs and batch verification of the extended elliptic curve digital signature technique to accelerate the verification process at selected trusted nodes. We demonstrate the security of our scheme and its resilience to secure clone node attack detection by conducting a comprehensive security analysis. The performance of our proposed scheme provides a high degree of detection accuracy with minimal detection time and significantly reduces the computation, communication and storage overhead.