Robust Spammer Detection Using Collaborative Neural Network in Internet-of-Things Applications

Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Muhammad Imran, Neeraj Kumar, DI Zhang, Keping Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)


Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.

Original languageEnglish
Article number9121286
Pages (from-to)9549-9558
Number of pages10
JournalIEEE Internet of Things Journal
Issue number12
Publication statusPublished - 2021 Jun 15


  • Collaborative awareness
  • Internet of Things (IoT)
  • neural network
  • spammer detection

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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