SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city

In today’s era, Sustainable city is evaluated using the services provided to the society. The designing of the integral part of the society should be focused towards the benefits of people. Internet is extensively utilized by the society using Search Engines. The accuracy and time it takes for different search engines to retrieve information from a cloud computing repository around the world, varies. However, it has been discovered in the literature that webpage ranking reduces the amount of time a user spends surfing, which saves a significant amount of energy during computation and transmission across the network. The hyperlink structure of the web graph is used in most of the earlier solutions documented in the literature, which consumes a lot of energy during calculation. It may exacerbate the link leakage problem by increasing the frequency of spam pages. In light of the energy consumption of various smart gadgets, hyperlink structure alone is no longer sufficient for predicting webpage relevance. Its true importance is revealed by user surfing activity. To improve search engine accuracy and speed, it is critical to demote spam pages, lowering energy consumption. Among all the existing ranking algorithms in the literature, one of the important components is the PageRank algorithm used by Google’s ranking module. Keeping focus on these points, in this paper, various page ranking algorithms based upon supervised learning are surveyed and summarized with respect to different selected parameters and experiments performed. Using this information, a detailed taxonomy of search engine results is presented in the text. Moreover, PageRank algorithms are explored by using different supervised learning techniques applied in the existing proposals for getting and processing the results. In the nutshell, the PageRank methodology is surveyed with respect to web spam detection which is the demand of cognitive systems in smart cities.