CYBER SECURITY THREAT DETECTION WITH BIG DATA

The Cyber Security Threat Detection with Big Data system provides a scalable, intelligent, and proactive security solution. By integrating big data technologies with hybrid machine learning models, the system overcomes the limitations of traditional reactive security frameworks. The platform improves detection of zero-day attacks, enhances analyst efficiency, and enables faster incident response. The proposed system provides a scalable, intelligent, and proactive cybersecurity solution.

1.    A Survey of Cyber Threat Attribution – Reviews challenges and techniques for identifying cyber threats. 2.     A Survey on Cyber Security Threats – Explains types of cyber threats like malware, phishing, and DDoS. 3.    Detection and Analysis of Cyber Security Threats Through Monitoring Tools – Discusses monitoring tools for detecting threats. 4.      Cybersecurity Threats and Countermeasures in Industrial IoT – Explains attacks and defenses in industrial IoT. 5.    AI-Driven Threat Detection Techniques – Shows how AI and machine learning detect cyber threats. 6.        Role of AI in Threat Detection (Palo Alto Networks) – Explains AI-based real-time threat detection. 7. AI Threat Detection (Sentinel One) – Describes AI systems identifying sophisticated cyberattacks. 8.     Real-Time Threat Detection Using AI (Cyber) – Shows AI detecting attacks on IoT, cloud, and mobile devices. 9.       Threat Detection and Response (Microsoft) – Defines steps to detect and respond to cyber threats. 10.               Advanced Persistent Threat Detection Techniques – Discusses methods for detecting long-term.