Review of a Unified Real-Time IDS and Mitigation Framework Using Apache Spark

Traditional intrusion detection systems are being surpassed by the increasingly sophisticated cyber threats that modern networks face. The increasing scale and complexity of modern network environments, coupled with the evolving sophistication of cyber threats, have rendered traditional Intrusion Detection Systems (IDS) inadequate for real-time and large-scale protection. This paper presents a comprehensive review and design strategy for a unified, real-time IDS and mitigation framework leveraging Apache Spark. This paper proposes a unified real-time IDS framework that utilizes Apache Spark to address the aforementioned disparity. The design combines threat intelligence, distributed machine learning, and streaming data analytics to facilitate automated mitigation and scalable multi-vector threat detection. We have identified critical limitations (e.g., offline detection, limited attack scope, outdated datasets) and have developed a set of objectives to address them through a review of current Spark-based IDS research. The outcome is a definitive roadmap for a next-generation IDS that offers low-latency, adaptive, and transparent defense in high-throughput network environments.