SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things
A far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy f...
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| Format: | Article |
| Language: | English English |
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Elsevier B.V.
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32300/ http://umpir.ump.edu.my/id/eprint/32300/1/SPY-BOT-Machine%20learning-enabled%20post%20filtering%20for%20social%20network.pdf http://umpir.ump.edu.my/id/eprint/32300/2/SPY-BOT-Machine%20learning-enabled%20post%20filtering%20for%20social%20network_FULL.pdf |
| _version_ | 1848823989342830592 |
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| author | Rahman, Md. Arafatur Zaman, Nafees Asyhari, A. Taufiq Sadat, M. S. Nazmus Pillai, Prashant Ruzaini, Abdullah Arshah |
| author_facet | Rahman, Md. Arafatur Zaman, Nafees Asyhari, A. Taufiq Sadat, M. S. Nazmus Pillai, Prashant Ruzaini, Abdullah Arshah |
| author_sort | Rahman, Md. Arafatur |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | A far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy for industrial revolution 4.0. With the integration of social networks with the Internet of Things being noticed in different industries to enhance human involvement and increase their productivity, security in such networks is increasingly alarming. Vulnerabilities can be characterized in the form of privacy invasion, leading to hazardous contents, which can be detrimental to social media actors and in turn impact the processes of the overall Social Network-Integrated Industrial Internet of Things (SN-IIoT) ecosystem. Despite this prevalence, the current platforms do not have any significant level of functionality to capture, process, and reveal unhealthy content among the social media actors. To address those challenges by detecting hazardous contents and create a stable social internet environment within IIoT, a statistical learning-enabled trustworthy analytic tool for human behaviors has been developed in this paper. More specifically, this paper proposes a machine learning (ML)-enabled scheme SPY-BOT, which incorporates a hybrid data extraction algorithm to perform post-filtering that arbitrates the users’ behavior polarity. The scheme creates class labels based on the featured keywords from the decision user and classifies suspicious contacts through the aid of ML. The results suggest the potential of the proposed approach to classify the users’ behavior in SN-IIoT. |
| first_indexed | 2025-11-15T03:05:54Z |
| format | Article |
| id | ump-32300 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:05:54Z |
| publishDate | 2021 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-323002025-07-22T01:51:21Z http://umpir.ump.edu.my/id/eprint/32300/ SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things Rahman, Md. Arafatur Zaman, Nafees Asyhari, A. Taufiq Sadat, M. S. Nazmus Pillai, Prashant Ruzaini, Abdullah Arshah QA76 Computer software A far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy for industrial revolution 4.0. With the integration of social networks with the Internet of Things being noticed in different industries to enhance human involvement and increase their productivity, security in such networks is increasingly alarming. Vulnerabilities can be characterized in the form of privacy invasion, leading to hazardous contents, which can be detrimental to social media actors and in turn impact the processes of the overall Social Network-Integrated Industrial Internet of Things (SN-IIoT) ecosystem. Despite this prevalence, the current platforms do not have any significant level of functionality to capture, process, and reveal unhealthy content among the social media actors. To address those challenges by detecting hazardous contents and create a stable social internet environment within IIoT, a statistical learning-enabled trustworthy analytic tool for human behaviors has been developed in this paper. More specifically, this paper proposes a machine learning (ML)-enabled scheme SPY-BOT, which incorporates a hybrid data extraction algorithm to perform post-filtering that arbitrates the users’ behavior polarity. The scheme creates class labels based on the featured keywords from the decision user and classifies suspicious contacts through the aid of ML. The results suggest the potential of the proposed approach to classify the users’ behavior in SN-IIoT. Elsevier B.V. 2021-07-09 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32300/1/SPY-BOT-Machine%20learning-enabled%20post%20filtering%20for%20social%20network.pdf pdf en http://umpir.ump.edu.my/id/eprint/32300/2/SPY-BOT-Machine%20learning-enabled%20post%20filtering%20for%20social%20network_FULL.pdf Rahman, Md. Arafatur and Zaman, Nafees and Asyhari, A. Taufiq and Sadat, M. S. Nazmus and Pillai, Prashant and Ruzaini, Abdullah Arshah (2021) SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things. Ad Hoc Networks, 121 (102588). pp. 1-11. ISSN 1570-8705. (Published) https://doi.org/10.1016/j.adhoc.2021.102588 https://doi.org/10.1016/j.adhoc.2021.102588 |
| spellingShingle | QA76 Computer software Rahman, Md. Arafatur Zaman, Nafees Asyhari, A. Taufiq Sadat, M. S. Nazmus Pillai, Prashant Ruzaini, Abdullah Arshah SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things |
| title | SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things |
| title_full | SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things |
| title_fullStr | SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things |
| title_full_unstemmed | SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things |
| title_short | SPY-BOT: Machine learning-enabled post filtering for social network-integrated industrial internet of things |
| title_sort | spy-bot: machine learning-enabled post filtering for social network-integrated industrial internet of things |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/32300/ http://umpir.ump.edu.my/id/eprint/32300/ http://umpir.ump.edu.my/id/eprint/32300/ http://umpir.ump.edu.my/id/eprint/32300/1/SPY-BOT-Machine%20learning-enabled%20post%20filtering%20for%20social%20network.pdf http://umpir.ump.edu.my/id/eprint/32300/2/SPY-BOT-Machine%20learning-enabled%20post%20filtering%20for%20social%20network_FULL.pdf |