Inspiration
Cyberattacks often begin with suspicious login attempts, repeated password failures, and unusual access patterns. We wanted to build a system that helps detect these threats early and makes login security easier to monitor.
What it does
Our AI-Powered Cybersecurity Login Monitoring System monitors login activities and identifies suspicious behaviour. It detects repeated failed login attempts, flags potential brute-force attacks, highlights unusual login patterns, and displays security alerts on a dashboard.
How we built it
We built a web-based monitoring dashboard using Python and Flask. The system processes login activity logs, analyses failed login attempts, and identifies suspicious patterns using rule-based detection, with an anomaly-detection module planned for further enhancement. The dashboard displays login statistics, potential threats, and security alerts.
Challenges we ran into
One major challenge was connecting the application to HBase for storing and retrieving login monitoring data. We also faced difficulties processing login events correctly and displaying security alerts in the dashboard. We worked on troubleshooting the connection and developing a mock-data mode for testing.
Accomplishments that we're proud of
We developed a prototype that demonstrates login activity monitoring, suspicious-attempt detection, and security alert visualisation. We are proud of turning cybersecurity concepts into a practical tool that can help administrators identify potentially dangerous login behaviour.
What we learned
We learned how to process login logs, detect suspicious activity, develop a Flask-based web application, and understand the challenges of integrating a database with a monitoring system. We also learned the importance of reliable testing and clear security visualisation.
What's next for atronova
We plan to integrate HBase reliably, add real-time log streaming, improve anomaly detection using machine learning, and introduce IP-based risk analysis and automated alerts. Our goal is to make the system more accurate, scalable, and useful for security teams.
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