BIG DATA BASED PREDICTIVE CYBER THREAT INTELLIGENCE SYSTEM

The proposed Big Data–based Predictive Cyber Threat Intelligence system offers a modern and efficient solution to handle increasing cyber threats. It shifts cybersecurity from a reactive approach to a proactive and predictive model. By leveraging big data analytics, the system can process massive volumes of structured and unstructured data. It integrates machine learning and artificial intelligence to identify patterns and detect anomalies effectively. The system improves the accuracy of threat detection compared to traditional methods. It significantly reduces false positives, helping security analysts focus on real threats. Real-time data processing enables faster identification and response to cyber-attacks. The system is highly scalable and can handle data from multiple sources such as networks, cloud, and IoT devices. It enhances visibility by integrating data across different platforms. Automated detection and response mechanisms reduce human effort and errors. The system also supports better risk assessment and decision-making. Predictive capabilities allow organizations to prevent attacks before they occur. It improves overall system reliability and performance. The use of advanced frameworks like Hadoop and Spark increases processing efficiency. Despite its advantages, challenges such as data privacy and computational cost still exist. Continuous research is required to improve model accuracy and efficiency. Future enhancements may include blockchain and advanced deep learning techniques. The system can be applied across various sectors like banking, healthcare, and government. It strengthens the overall cybersecurity infrastructure of organizations. In conclusion, the proposed system provides a powerful and intelligent approach to secure digital environments against evolving cyber threats.  

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