Artificial Intelligence and Edge Computing-Enabled Web Spam Detection for Next Generation IoT Applications
Aaisha Makkar, Uttam Ghosh, Pradip Kumar Sharma
Abstract
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%.