The Next‐Generation NIDS Platform: Cloud‐Based Snort NIDS Using Containers and Big Data

Ferry Astika Saputra, Muhammad Salman, Jauari Akhmad Nur Hasim, Isbat Uzzin Nadhori, Kalamullah Ramli

Research output: Contribution to journalArticlepeer-review

Abstract

Snort is a well‐known, signature‐based network intrusion detection system (NIDS). The Snort sensor must be placed within the same physical network, and the defense centers in the typical NIDS architecture offer limited network coverage, especially for remote networks with a restricted bandwidth and network policy. Additionally, the growing number of sensor instances, followed by a quick increase in log data volume, has caused the present system to face big data challenges. This research paper proposes a novel design for a cloud‐based Snort NIDS using containers and implementing big data in the defense center to overcome these problems. Our design consists of Docker as the sensor’s platform, Apache Kafka, as the distributed messaging system, and big data technology orchestrated on lambda architecture. We conducted experiments to measure sensor deploy-ment, optimum message delivery from the sensors to the defense center, aggregation speed, and efficiency in the data‐processing performance of the defense center. We successfully developed a cloud‐based Snort NIDS and found the optimum method for message‐delivery from the sensor to the defense center. We also succeeded in developing the dashboard and attack maps to display the attack statistics and visualize the attacks. Our first design is reported to implement the big data architecture, namely, lambda architecture, as the defense center and utilize rapid deployment of Snort NIDS using Docker technology as the network security monitoring platform.

Original languageEnglish
Article number19
JournalBig Data and Cognitive Computing
Volume6
Issue number1
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Big data
  • Cloud‐based IDS
  • Docker
  • Lambda architecture
  • Snort

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