Intelligent Web Spam Detection in the Consumer Internet of Things
Rong Wang, Zhuang Xu, Zhu Xiao-gang, Ali Kashif Bashir, Maryam Mohamed Al Dabel, Keping Yu · IEEE Transactions on Consumer Electronics · 2024
The Consumer Internet of Things (CIoT) integrates the advantage of Internet of Things (IoT) technologies to provide convenience in consumers’ daily lives. With the rapid development of the CIoT, data collected from consumer smart devices has increased exponentially. In the CIoT, Web pages, as Internet information carriers, offer spammers opportunities to conduct security attacks, which could harm the CIoT systems. Inspired by this challenge, this paper introduces an intelligent feature extraction method, page2vec, and a new classification algorithm, RFiRF, to detect Web spam in the CIoT. page2vec is based on a score propagation model, which calculates each page’s goodness and badness scores through the links of a Web graph. It is observed that different scoring functions can produce different Web page features. Based on this observation, 20 to 30 scoring functions are designed and incorporated into page2vec. This way, page2vec can automatically extract Web page features from a Web graph. Unfortunately, page2vec also brings a high dimensionality problem when constructing binary classifiers for spam detection. To address this problem, a new classification algorithm, called Random Forest in Random Forest (RFiRF), is proposed. RFiRF replaces a random forest’s meta-classifier (decision tree) with a random forest. It divides the sparse data space into dense sub-spaces by randomly sampling training instances and features. Experiments on two benchmark datasets show that 1) page2vec features are much more predictive than the raw link-based and node2vec features, and 2) RFiRF outperforms some classification algorithms in most cases when facing the high dimensionality problem. We hope this paper can give peers valuable insights into the CIoT security.