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Processing high volumes of security data efficiently is crucial for Managed Security Service Providers (MSSPs). A parallel framework distributing events and queries across instances optimizes performance for real-time threat detection.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Distributed Systems

Background:

  • Real-time data processing for security presents performance challenges, particularly with limited hardware.
  • Managed Security Service Providers (MSSPs) face high event rates (hundreds to thousands of events per second).
  • Efficient data processing is critical for timely attack identification and response.

Purpose of the Study:

  • Evaluate the performance of the OSTROM security framework using the Esper complex event processing (CEP) engine.
  • Compare parallel and non-parallel computational frameworks for event processing.
  • Investigate the impact of different architectures on throughput, memory, and CPU usage.

Main Methods:

  • Implemented three distinct architectures for Esper event processing.
  • Evaluated system performance under varying event rates and query loads.
  • Measured throughput, memory consumption, and CPU utilization for each configuration.

Main Results:

  • System performance is primarily constrained by incoming event volume, not query complexity.
  • An architecture distributing 1/4th of events to each instance while processing all queries across all units yielded optimal results.
  • This configuration demonstrated superior throughput and reduced memory and CPU usage.

Conclusions:

  • The chosen parallel architecture significantly enhances the efficiency of real-time security data processing.
  • Optimized event distribution and parallel query processing are key to overcoming performance bottlenecks.
  • Findings provide valuable insights for MSSPs seeking to improve their security infrastructure on limited hardware.