LIVE LOGIN ANOMALY DETECTOR

Abstract

The Live Login Anomaly Detector is an intelligent cybersecurity solution designed to enhance authentication mechanisms by detecting suspicious login activities in real time. Traditional authentication systems that rely only on usernames and passwords are increasingly vulnerable to cyber threats such as phishing, brute-force attacks, and credential stuffing. To address these challenges, the proposed system incorporates behavioral analysis by monitoring login parameters such as IP address, device information, login time, location, and frequency of attempts. By analyzing these features, the system assigns a dynamic risk score to each login attempt, categorizing it as low, medium, or high risk. Based on this classification, appropriate actions are taken, including granting access, requesting additional verification such as multifactor authentication, or blocking the login attempt. The system utilizes machine learning techniques to improve detection accuracy over time by learning from historical user behavior patterns. It follows a modular and scalable architecture that supports real-time processing and seamless integration with existing applications. Technologies such as Python, Flask, and database systems are used to ensure efficient performance and secure data handling. Additionally, features like token-based authentication and rate limiting enhance overall system security. The solution is suitable for deployment across various domains, including banking, e-commerce, and enterprise systems, where data security is critical. Overall, the system provides a proactive, adaptive, and efficient approach to authentication security while maintaining a smooth user experience.