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Anomaly Detection Against Shilling Attacks In E-Com Site

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Anomaly Detection Against Shilling Attacks In E-Com Site

Personalized recommendations have been instrumental in enhancing customer satisfaction and benefits across various types of web applications. However, shilling attackers have become increasingly rampant, leveraging biased rating profiles to manipulate item recommendations. This has led to a decrease in recommending precision and user satisfaction, as well as damage to the trustworthiness of intermediated transaction platforms and participants.While many studies have proposed methods to combat shilling attacks, such as user profile-based detection, these methods suffer from the extraction of universal attacker features, resulting in poor performance against improved shilling attack types. To address these issues, this paper introduces a novel dynamic time interval segmentation technique-based item anomaly detection approach.Inspired by the common attack features from the item profile standpoint, this approach can detect attacks regardless of the specific attack type. Anomaly Detection Against Shilling Attacks In E-Com Site The proposed segmentation technique dynamically confirms the size of the time interval to group as many consecutive attack ratings together as possible. Moreover, in addition to effectiveness metrics, the study introduces a stability metric to measure the accuracy and stability of detection methods.Thorough experiments on the Movie Lens dataset demonstrate the performance of the proposed approach and highlight its value for online applications.

below video its a version of web u can get in android as well android based app projects for college students

Software Requirements: –
Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Android Tools:
IDE: Android Studio
Android Emulator
XAMPP 8.1 – 64 bit

PHP Tools:
XAMPP 8.1 – 64 bit