An graph-based adaptive method for fast detection of transformed data leakage in IOT Via WSN

Abstract

As statistics show that most threats to information security in Internet of Things (IOT) are caused by data leakage, lots of methods have been developed to address the problem of data leakage prevention (DLP). However, most of these methods do not work well when the confidentiality of data changes frequently. We propose an Adaptive Feature Graph Update model (AFGU) to solve the problem by mapping the features of confidential data to the feature graph. First, the feature graph are built to record the features of confidential data which involve the sensitive terms and their context. Then, the improved evaluation method for the importance of each term is employed to update the feature graph according to the importance degree of each term. Finally, the confidentiality of data are determined by matching the features of the data with the feature graph. Experiments results show that the proposed method can detect confidential data effectively and efficiently.