Publication

Abstract : Intrusion Detection Systems (IDS) are critical for identifying and mitigating potential security threats within network traffic. However, traditional IDS solutions often struggle with scalability and real-time threat detection, particularly in high-volume, high-velocity environments. The proposed work introduces a scalable and efficient IDS that leverages Apache Kafka and Apache Spark to address these challenges. Kafka's robust streaming platform ensures reliable, low-latency data flow, while Apache Spark's parallelized machine learning algorithms (MLlib) enable rapid and accurate classification of network traffic. By combining Kafka's data handling capabilities with Spark's processing efficiency, the proposed system provides fast, adaptive threat detection in real- time. This approach not only enhances IDS performance but also sets the stage for future developments in scalable, distributed IDS solutions. The work demonstrates the potential of big data technologies to improve network security in complex and dynamic network environments.