The mobile botnet, developed from the traditional PC-based botnets, has become a practical underlying trend. In this paper, we design a mobile botnet, which exploits a novel command and control (C&C) strategy named Push-Styled C&C. It utilizes Google cloud messaging (GCM) service as the botnet channel. Compared with traditional botnet, Push-Styled C&C avoids direct communications between botmasters and bots, which makes mobile botnets more stealthy and resilient. Since mobile devices users are sensitive to battery power and traffic consumption, Push- Styled botnet also applies adaptive network connection strategy to reduce traffic consumption and cost. To prove the efficacy of our design, we implemented the prototype of Push-Style C&C in Android. The experiment results show that botnet traffic can be concealed in legal GCM traffic with low traffic cost.
在线社会网络已经成为社会学和信息科学的数据宝库,但是直接分析社会网络数据会造成敏感信息泄漏,对用户隐私构成威胁。传统的基于数据匿名化技术的隐私保护技术面对不断提高的背景攻击显得无能为力。对此,差分隐私作为一种可以严格定义的可量化技术被引入到社会网络的隐私保护中。文中提出一种基于层次随机图(Hierarchical Random Graph)的满足ε-差分隐私的社会网络图发布算法DP-HRGP(Differential Privacy-Hierarchical Random Graph Publishing)。该算法的噪声增加机制分为两个阶段:首先通过指数机制计算HRG结构树的得分,并利用马尔科夫蒙特卡洛(Markov Chain Monte Carlo)方法进行采样得到HRG结构树候选集合,然后通过拉普拉斯机制对稳态采样集合中的HRG的内部节点进行加噪,将加噪后的HRG转化为下三角矩阵,并求出所有稳态采样HRG的下三角均值矩阵,最后,根据均值矩阵内元素值即层次随机图的内部节点的连接概率值生成净化后的社会网络发布图。实验证明了DP-HRGP算法在满足ε-差分隐私的同时具有较好的数据可用性。
