论文标题
使用流量感知机器学习方法预测物联网应用中敏感信息泄漏
Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach
论文作者
论文摘要
本文提出了一种识别脆弱物联网应用程序的方法。该方法着重于导致敏感信息泄漏的漏洞类别，可以使用污点流分析来识别。污染的流量脆弱性受到程序结构和代码中的陈述顺序的影响很大，设计一种检测此类漏洞的方法需要考虑此类信息，以便提供精确的结果。在本文中，我们提出并开发了一种方法，即FlostMiner，除了与程序的语句顺序外，该矿山从与程序结构（例如控制语句和方法）相关的代码中提出了特征。流量器，以污染流的形式生成特征。我们开发了Flows2VEC，该工具将流量器恢复的功能转换为向量，然后通过提供流动的模型构建过程来帮助它们来帮助机器学习过程。如果源代码中的声明顺序显示漏洞，则最终的模型能够将应用程序准确地将应用程序分类为脆弱性。与单词（BOW）方法的基本袋相比，实验表明，所提出的方法改善了所有算法的预测模型的AUC，并且Colpus1数据集的最佳案例从0.91提高到0.94，并且对于Colpus2，并且从0.56提高到0.96
This paper presents an approach for identification of vulnerable IoT applications. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis. Tainted flows vulnerability is very much impacted by the structure of the program and the order of the statements in the code, designing an approach to detect such vulnerability needs to take into consideration such information in order to provide precise results. In this paper, we propose and develop an approach, FlowsMiner, that mines features from the code related to program structure such as control statements and methods, in addition to program's statement order. FlowsMiner, generates features in the form of tainted flows. We developed, Flows2Vec, a tool that transform the features recovered by FlowsMiner into vectors, which are then used to aid the process of machine learning by providing a flow's aware model building process. The resulting model is capable of accurately classify applications as vulnerable if the vulnerability is exhibited by changes in the order of statements in source code. When compared to a base Bag of Words (BoW) approach, the experiments show that the proposed approach has improved the AUC of the prediction models for all algorithms and the best case for Corpus1 dataset is improved from 0.91 to 0.94 and for Corpus2 from 0.56 to 0.96
