Real-Time Cyber Threat Detection Using Big Data Analytics: A Scalable Framework for Immediate Threat Response

The increasing complexity of cyber threats across industries such as human resources (HR), financial services, and government agencies necessitates the development of real time security solutions. Traditional security measures such as rule based intrusion detection systems (IDS) and batch processing analytics are inadequate for detecting dynamic and sophisticated cyber threats, including insider attacks, financial fraud, and government infrastructure intrusions. This paper presents a scalable real time cyber threat detection framework that integrates big data analytics, machine learning, and automated incident response mechanisms to detect and mitigate threats in real time. The proposed system consists of secure data ingestion pipelines, distributed real time processing using Apache Kafka and Spark Streaming, and an ensemble machine learning based anomaly detection model. A cross industry use case highlights the framework’s ability to detect insider threats in HR systems, fraudulent financial transactions, and cyber espionage in government networks. The system is implemented on AWS cloud infrastructure, and experimental results show that it achieves 98.6% precision for HR insider threat detection, 94.2% accuracy in financial fraud prevention, and a sub-2-second detection latency for public sector security alerts. This paper also discusses the scalability of the system, highlighting its ability to process over 50,000 security events per second while maintaining real-time performance. The results indicate that big data-driven cybersecurity solutions can significantly improve threat detection, response times, and overall security posture across different industries.