文献类型：会议论文

英文题名：RL-Gen: A Character-Level Text Generation Framework with Reinforcement Learning in Domain Generation Algorithm Case

作者：Cheng, Hua[1];Cai, Jing[1];Fang, Yiquan[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

会议日期：DEC 12-15, 2019

会议地点：Sydney, AUSTRALIA

语种：英文

外文关键词：Domain Generation Algorithm; DGA detection; WGAN-GP; Reinforcement Learning; Character-level text generation

摘要：Malware families often use the Domain Generation Algorithm (DGA) to communicate with the Command and Control (C&C) servers. Although machine learning and deep learning based methods have achieved good accuracy in DGA detection task, it has problems on the new DGA families with limited datasets. In this paper, RL-Gen, a Reinforcement Learning (RL) framework, is proposed to improve the performance of character-level text generation with few input samples. RL-Gen has two modules, W-Generator and Evaluator. W-Generator is an improved generation model based on WGAN-GP, which is regarded as an agent, and Evaluator acts as an environment to evaluate the generated text. Especially in DGA case, Evaluator is an effective DGA detection model (ATT-GRU). The parameters' updating of W-Generator is optimized by the reward from Evaluator, which promotes the generating abilities on speed and quality. Experiments show that the generated DGAs are sufficiently close to real DGAs, and can play an alternative role of real DGAs in detection model training. And RL-Gen gets better quality of text generation more quickly and smoothly than WGAN-GP.