Generating Payloads of Power Monitoring Systems Compliant with Power Network Protocols Using Generative Adversarial Networks

In the network environment of power systems, payload generation is used to construct data packets, which are used to obtain data for the security management of network assets. Payloads generated by existing methods cannot satisfy the specifications of the protocols in power systems, resulting in low efficiency and information errors. In this paper, a payload generation model, LoadGAN, is proposed by using generative adversarial networks (GANs). Firstly, we find segmentation points to cut payloads into different segment sequences using sliding window schema based on Bayesian optimization. Then, we use different payload segments to train several child generators to generate corresponding parts of a whole payload. Segment sequences generated by these generators are assembled to form a whole new payload that is compliant with the specifications of the original network protocol. Experiments on the Mozi botnet dataset show that LoadGAN achieves precise payload segmentation while maintaining a high payload effectiveness of 85.5%, which is a 40% improvement compared to existing methods.