Protecting Against Malicious Code Injection in Reviews on Web Applications

Abstract

Malware-code injection attack primarily evaluated by an attacker or hacker. These fake user IDs created by continuously sending user links until the attacker clicks on the link. By deliberately clicking on the included link, an attacker steals the user's identity, similar to phishing. These fraudulently inserted reviews are usually confused with the original user reviews of the product on the website. This study uses the Naive Bayes Classifier (NBC) method to detect malicious rating injection attacks. Then use natural language processing techniques to remove unwanted information from your web pages. Natural Language Processing (NLP) is a way to remove unwanted words from a document. Then, for ease of understanding, use the Principal Component Analysis (PCA) algorithm to reduce the score length for a particular sample dataset. This reduces the score dimension. The user-entered ratings are then compared to the sample dataset and categorized into good and bad ratings. It also uses the naive Bayes classifier algorithm used to classify objects and is not detected as malicious.