A Fuzzy-Based Approach to Enhance Cyber Defence Security for Next-Generation IoT

In the modern era, the Cognitive Internet of Things (CIoT) in conjunction with IoT evolves which provides the intelligence power of sensing and computation for next-generation IoT (Nx-IoT) networks. The data scientists have discovered a large amount of techniques for knowledge discovery from processed data in CIoT. This task is accomplished successfully and data proceeds for further processing. The major cause for the failure of IoT devices is due to the attacks, in which Web spam is more prominent. There seems a requirement of a technique which can detect the Web spam before it enters into a device. Motivated from these issues, in this article, a cognitive spammer framework (CSF) for Web spam detection is proposed. CSF detects the Web spam by fuzzy rule-based classifiers along with machine learning classifiers. Each classifier produces the quality score of the webpage. These quality scores are then ensembled to generate a single score, which predicts the spamicity of the webpage. For ensembling, the fuzzy voting approach is used in CSF. The experiments were performed using a standard data set WEBSPAM-UK 2007 with respect to accuracy and overhead generated. From the results obtained, it has been demonstrated that CSF improves the accuracy by 97.3%, which is comparatively high in comparison to the other existing approaches in the literature.

Automated summary

In this article, a cognitive spammer framework (CSF) for Web spam detection is proposed, which detects Web spam using fuzzy rule-based classifiers and machine learning classifiers. CSF uses the fuzzy voting approach to ensemble the quality scores of different classifiers and predict the spamicity of webpages. Experimental results show that CSF improves the accuracy by 97.3% compared to existing approaches.