An Efficient Technique for Detecting Vulnerabilities in Malicious Websites

With the rapid growth of online platforms and social networking systems, cybersecurity threats such as phishing, malware injection, Cross-Site Scripting (XSS), SQL Injection, and brute-force attacks have become increasingly prevalent. This paper presents an efficient and intelligent web-based system developed using Python that integrates two major modules: (1) a Malicious Website Detection Module and (2) a Secure Social Media Platform Module with real-time attack prevention mechanisms. In the first module, users can submit a website URL, and the system analyzes it using machine learning–based feature extraction techniques, blacklist verification, and URL structure analysis to determine whether the website is malicious or legitimate. The detection model evaluates lexical, host-based, and content-based features to improve accuracy and reduce false positives. The second module simulates a secure social networking environment where users can register, log in, create posts, edit or delete them, and interact through likes and comments. The system continuously monitors user inputs and behavior patterns to detect potential security threats such as XSS attacks, brute-force login attempts, Cross- Site Scripting payload injections, and SQL injection attempts. If malicious activity is detected, the system automatically blocks the offending user and logs the event for administrative review. The proposed system employs Python frameworks such as Flask/Django for backend development, machine learning algorithms such as Random Forest or Support Vector Machine for classification, and input validation and sanitization techniques to prevent injection attacks. The integrated approach ensures proactive detection, real-time prevention, and enhanced platform security. Experimental evaluation demonstrates that the system effectively detects malicious URLs and prevents common web application attacks, thereby providing a secure digital environment for users.