Wake-Up Security: Effective Security Improvement Mechanism for Low Power Internet of Things

Abstract

As time and space constraints decrease due to the development of wireless communication network technology, the scale and scope of cyberattacks targeting the Internet of Things (IoT) are increasing. However, it is difficult to apply high-performance security modules to the IoT owing to the limited battery, memory capacity, and data transmission performance depending on the size of the device. Conventional research has mainly reduced power consumption by lightening encryption algorithms. However, it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security performance. In this study, we propose wake-up security (WuS), a low-power security architecture that can utilize high-performance security algorithms in an IoT environment. By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result, WuS improves security and power efficiency while using a relatively high-complexity security module in a lowpower environment compared to the conventional method of periodically executing a high-performance security module. In this study, a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption, latency, and security of the proposed method. The evaluation results reveal that the power consumption of the proposed WuS mechanism is approximately 51.8% and 27.2% lower than those of conventional high-performance security and lightweight security modules, respectively. Additionally, the latencies are approximately 74.8% and 65.9% lower, respectively. Furthermore, the WuS mechanism achieved a high detection accuracy of approximately 96.5% or greater, proving that the detection efficiency performance improved by approximately 33.5% compared to the conventional model. The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model. Therefore, they can be used in various ways by selecting suitable models based on the performance levels required in each industry.