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*

*この研究の対応する著者

研究成果: Article査読

30 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号9121286
ページ(範囲)9549-9558
ページ数10
ジャーナルIEEE Internet of Things Journal
8
12
DOI
出版ステータスPublished - 2021 6 15

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

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