An Automatic and Efficient Malware Traffic Classification Method for Secure Internet of Things

Malware traffic classification (MTC) plays an important role in cyber security and network resource management for the secure Internet of Things (IoT). Many deep learning (DL)-based MTC methods have been proposed due to their robustness and effectiveness with self-designed model architecture. However, to completely adjust complex parameters in the DL model, the architecture design of the DL model requires substantial professional knowledge and effort from human experts. To solve these problems, we propose an automatic and efficient MTC method using neural architecture search via proximal iterations (NASP), which can automatically and efficiently search the optimal model architecture according to the network traffic in the realistic environment. Specifically, we first describe NAS as a constrained optimization problem by keeping the search space differentiable and forcing the architecture to be discrete in the search process. Second, a suitable regularizer is introduced to balance the complexity and performance of the model architecture. Finally, the simulation results show that the proposed NASP-aided MTC method not only can efficiently and accurately search the optimal classification model architecture on the USTC-TFC2016 data set and the Egde-IIoTset data set but also compared with the typical MTC methods it can achieve the optimal classification performance with the fewer parameters as well as the floating-point operations (FLOPs).