Cybersecurity

Protecting digital systems, users, and data from evolving cyber threats. Click on the respective problem statement to view it's description.

Problem

Phishing attacks via emails, SMS, and fake websites are becoming increasingly sophisticated. Traditional detection methods often fail to recognize evolving patterns, leaving users and organizations vulnerable. A real-time, adaptive detection system is needed to prevent data breaches and financial losses.

Description

The challenge is to design an AI-based system capable of analyzing message content, URLs, sender behavior, and access patterns to detect phishing attempts. The system should minimize false positives while ensuring high detection accuracy. It must adapt to new attack vectors, recognize subtle anomalies, and provide actionable alerts to end-users or administrators. The platform should also scale to monitor large volumes of communications across multiple channels, helping organizations proactively prevent phishing incidents.

Problem

Organizations generate massive logs from servers, applications, and network devices, but lack centralized monitoring. Suspicious activities like failed logins or unauthorized access often go unnoticed, causing delayed detection of security incidents.

Description

The challenge is to build a centralized log monitoring system that collects logs from multiple sources in real time. The system should detect anomalies and potential security threats using rule-based or AI-assisted mechanisms. It must provide configurable alerts, highlight critical incidents, and support easy visualization through dashboards. Scalability and reliability are key, as the system should handle high volumes of logs while helping security teams respond proactively to prevent breaches.

Problem

Phishing attacks increasingly target users through Hinglish and regional Indian languages, bypassing traditional English-centric detection systems. Users are exposed to fraudulent messages, impersonation attempts, and malicious links.

Description

The challenge is to develop a multilingual phishing detection system that can analyze code-mixed and transliterated messages. The system should identify social engineering cues, urgency triggers, and suspicious links while minimizing false positives. It should provide explainable alerts and support security teams in protecting users across diverse language contexts. The platform should be scalable and adaptable to different communication channels to safeguard organizations and individuals effectively.