Real-time Pattern Detection in IP Flow Data using Apache Spark

Detection of network attacks is a challenging task, especially concerning detection coverage and timeliness. The defenders need to be able to detect advanced types of attacks and minimize the time gap between the attack detection and its mitigation. To meet these requirements, we present a stream-based IP flow data processing application for real-time attack detection using similarity search techniques. Our approach extends capabilities of traditional detection systems and allows to detect not only anomalies and attacks that match exactly to predefined patterns but also their variations. The approach is demonstrated on detection of SSH authentication attacks. We describe a process of patterns definition and illustrate their usage in a real-world deployment. We show that our approach provides sufficient performance of IP flow data processing for real-time detection while maintaining versatility and ability to detect network attacks that have not been recognized by traditional approaches.