文献类型：期刊文献

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 University of Science and Technology, Shanghai, China

年份：2019

卷号：1143 CCIS

起止页码：690

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20200508098906)

语种：英文

外文关键词：Quality control - Deep learning - Learning algorithms - Command and control systems

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. ? Springer Nature Switzerland AG 2019.