The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection

Conference paper

Makkar, A., Kumar, N. and Guizani, M. 2019. The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection. 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019; Xiamen; China; 6 December 2019 through 9 December 2019; Category numberCFP19COI-ART; Code 157933. IEEE. https://doi.org/10.1109/ssci44817.2019.9002885
AuthorsMakkar, A., Kumar, N. and Guizani, M.
TypeConference paper
Abstract

In the modern era, Internet of Things(IoT) plays an important role in connecting the people across the globe. The IoT objects enable the communication and data exchange among each other irrespective of their geographical locations. In such an environment, the Web of Things (WoT) provides the Internet service to the IoT objects. The Internet is mostly accessed by the search engines. The success of search engine depends upon the ranking algorithm. Although, Google is preferred by the maximum Internet users, but still the Google's ranking algorithm, PageRank experiences the occurrence of spam web pages. In this paper, the webpage filtering algorithm is proposed which automatically detects the spam web pages. The spam webpages are detected before these are processed by the ranking module of search engines. The machine learning model, i.e., decision tree is used for the validation of the proposed scheme. The ten fold cross validation approach is used to improve the accuracy of model, i.e., 98.2%. The results obtained demonstrate that the proposed scheme has the power of preventing the spam web pages in Cognitive Internet of Things (CIoT) environment.

Keywordsweb spam; IoT; AI; CIoT; WoT
Year2019
Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019; Xiamen; China; 6 December 2019 through 9 December 2019; Category numberCFP19COI-ART; Code 157933
Journal2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/ssci44817.2019.9002885
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85080955400&partnerID=MN8TOARS
Journal citationDec 2019 (Article 9002885), pp. 3132-3138
ISBN978-172812485-8
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8975711/proceeding
Output statusPublished
Publication dates
Online20 Feb 2019
Publication process dates
Deposited22 May 2023
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