- Title
- Robust spammer detection using collaborative neural network in internet-of-things applications
- Creator
- Guo, Zhiwei; Shen, Yu; Bashir, Ali; Imran, Muhammad; Kumar, Neeraj; Zhang, Di; Yu, Keping
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184677
- Identifier
- vital:16538
- Identifier
-
https://doi.org/10.1109/JIOT.2020.3003802
- Identifier
- ISBN:2327-4662 (ISSN)
- Abstract
- 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. © 2014 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Internet of Things Journal Vol. 8, no. 12 (2021), p. 9549-9558
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Subject
- 4006 Communications Engineering; 4606 Distributed Computing and Systems Software; 4009 Electronics, Sensors and Digital Hardware; Collaborative awareness; Internet of Things (IoT); Neural network; Spammer detection
- Full Text
- Reviewed
- Funder
- This work was supported in part by the State Language Commission Research Program of China under Grant YB135-121; in part by the Chongqing Natural Science Foundation of China under Grant cstc2019jcyj-msxmX0747; in part by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044; and in part by the Key Research Project of Chongqing Technology and Business University under Grant ZDPTTD201917, Grant KFJJ2018071, and Grant 1856033. The work of Muhammad Imran was supported by the Deanship of Scientific Research, King Saud University through the Research Group under Project RG-1435-051.
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