DGA-Based Botnet Detection Toward Imbalanced Multiclass Learning

Botnets based on the Domain Generation Algorithm(DGA) mechanism pose great challenges to the main current detection methods because of their strong concealment and robustness. However, the complexity of the DGA family and the imbalance of samples continue to impede research on DGA detection. In the existing work, the sample size of each DGA family is regarded as the most important determinant of the resampling proportion;thus,differences in the characteristics of various samples are ignored, and the optimal resampling effect is not achieved.In this paper, a Long Short-Term Memory-based Property and Quantity Dependent Optimization(LSTM.PQDO)method is proposed. This method takes advantage of LSTM to automatically mine the comprehensive features of DGA domain names. It iterates the resampling proportion with the optimal solution based on a comprehensive consideration of the original number and characteristics of the samples to heuristically search for a better solution around the initial solution in the right direction;thus, dynamic optimization of the resampling proportion is realized.The experimental results show that the LSTM.PQDO method can achieve better performance compared with existing models to overcome the difficulties of unbalanced datasets;moreover, it can function as a reference for sample resampling tasks in similar scenarios.

喀麦隆新华商的在地适应与创业策略

本文将自20世纪80年代以来在喀麦隆创业的中国大陆移民称为新华商。基于30个案例的深度访谈资料,本文运用机会结构与族裔特征互动模型分析喀麦隆新华商的在地适应与创业策略。研究发现,面对喀麦隆市场开放、国家鼓励外来投资和工业基础薄弱等机会结构,新华商在创业时不仅充分利用了中国轻工业生产与出口的优势,而且还动用了喀麦隆华人社团的强大支持等族裔资本,形成了跨国连接与在地镶嵌两大创业策略。而发展在地社会网络的同时不寻求国籍身份转变、不融入本地社群可以界定为新华商群体的最重要特征。喀麦隆新华商的创业实践反映出“南-南”移民流动框架下国家关系具有平等互惠性,进而使移民跨国创业行动有了更大的策略选择空间。