Artificial Intelligence and Edge Computing-Enabled Web Spam Detection for Next Generation IoT Applications

Autor:	Pradip Kumar Sharma, Uttam Ghosh, Aaisha Makkar
Rok vydání:	2021
Předmět:	Edge device business.industry Computer science Cloud computing computer.software_genre Spamdexing Server The Internet Data mining Enhanced Data Rates for GSM Evolution Electrical and Electronic Engineering business Instrumentation computer Image gradient Edge computing
Zdroj:	IEEE Sensors Journal. 21:25352-25361
ISSN:	2379-9153 1530-437X
DOI:	10.1109/jsen.2021.3066492
Popis:	For next generation IoT applications, edge devices provides most of the computing resources close to the proximity of the end users. These devices having built-in intelligence using various AI techniques can take independent decisions in the environment where these are deployed. Motivated from these concerns, We suggest a cognitive intrusion security system to maintain the credibility of search engine results, which eliminates the advertising images from penetrating the image database of the web browser. The proposed framework provides edge intelligence for web data filteration and detects the web spam by considering three different layers, i.e., data collection services, edge computing services, and cloud services. The target is to detect the malicious images. Firstly, the features of an image such as mean, image gradient, entropy are fetched and then the retrieved data is processed in the proposed framework. Deep learning algorithms are used for the validation of the proposed system. By evaluating it on real-time collected dataset, it resulted in an accuracy of 98.77%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::dc1918a21bb36cfda416df5d18a59077 https://doi.org/10.1109/jsen.2021.3066492 Zobrazit plný text záznamu