A Fuzzy-based approach to Enhance Cyber Defence Security for Next-generation IoT

Aaisha Makkar, Uttam Ghosh, Pradip Kumar Sharma, Amir Javed

Research output: Contribution to journalArticlepeer-review

Abstract

In modern era, 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 paper, 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 web page. For ensembling, fuzzy voting approach is used in CSF. The experiments were performed using standard dataset 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 literature.

Original languageEnglish
JournalIEEE Internet of Things Journal
Early online date21 Jan 2021
DOIs
Publication statusE-pub ahead of print - 21 Jan 2021

Keywords

  • Cognition
  • cognitive
  • Computational modeling
  • Ensemble
  • fuzzy
  • Internet of Things
  • Machine learning
  • Search engines
  • Unsolicited e-mail
  • Web pages
  • web spam

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