Fine-grained Web Service Trust Detection: A Joint Method of Machine Learning and Blockchain

Abstract

Current website defacement detection methods often ignore security and credibility in the detection process. Furthermore, with the gradual development of dynamic websites, false positives and underreports of website defacement have periodically occurred. Therefore, to enhance the credibility of website defacement detection and reduce the false-positive rate and the false-negative rate of website defacement, this paper proposes a fine-grained trust detection scheme called WebTD, that combines machine learning and blockchain. WebTD consists of two parts: an analysis layer and a verification layer. The analysis layer is the key to improving the success rate of website defacement detection. This layer mainly uses the naive Bayes (NB) algorithm to decouple and segment different types of web page content, and then preprocess the segmented data to establish a complete analysis model. Second, the verification layer is the key to establishing a credible detection mechanism. WebTD develops a new blockchain model and proposes a multi-value verification algorithm to achieve a multilayer detection mechanism for the blockchain. In addition, to quickly locate and repair the defaced data of the website, the Merkle tree (MT) algorithm is used to calculate the preprocessed data. Finally, we evaluate WebTD against two state-of-the-art research schemes. The experimental results and the security analysis show that WebTD not only establishes a credible web service detection mechanism but also keeps the detection success rate above 98%, which can effectively ensure the integrity of the website.