Identification of Active Attacks in Internet of Things: Joint Model- and Data-Driven Automatic Modulation Classification Approach

The Internet of Things (IoT) pervades every aspect of our daily lives and industrial productions since billions of interconnected devices are deployed everywhere of the globe. However, the seamless IoT unveils a number of physical-layer threats, such as jamming and spoofing that decrease the communication performance and the reliability of the IoT systems. As the process of identifying the modulation format of signals corrupted by noise and fading, automatic modulation classification (AMC) plays a vital role in physical-layer security as it can detect and identify the pilot jamming, deceptive jamming, and sybil attacks. In this article, we propose a novel cyclic correntropy vector (CCV)-based AMC method using long short-term memory densely connected network (LSMD). Specifically, cyclic correntropy model-driven feature CCV is first extracted using the received signals as it contains both the second-order and the higher order characteristics of cyclostationary. Then, the extracted CCV feature is put into the data-driven LSMD which mainly consists of long short-term memory (LSTM) network and dense network (DenseNet). Moreover, an additive cosine loss is utilized to train the LSMD for maximizing the interclass feature differences and minimizing the intraclass feature variations. Simulations demonstrate that the proposed CCV-LSMD method yields superior performance than other recent schemes.

Automated summary

The article proposes a novel cyclic correntropy vector (CCV)-based automatic modulation classification (AMC) method using long short-term memory densely connected network (LSMD), demonstrating superior performance compared to recent schemes. The method extracts CCV features from received signals and utilizes data-driven LSMD with an additive cosine loss to train the model for maximizing interclass feature differences and minimizing intraclass feature variations.