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Filtering intrusion detection alarms

Abstract

A Network Intrusion Detection System (NIDS) is an alarm system for networks. NIDS monitors all network actions and generates alarms when it detects suspicious or malicious attempts. A false positive alarm is generated when the NIDS misclassifies a normal action in the network as an attack. We present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by a NIDS. Our data mining technique is based on a Growing Hierarchical Self-Organizing Map (GHSOM) that adjusts its architecture during an unsupervised training process according to the characteristics of the input alarm data. GHSOM clusters these alarms in a way that supports network administrators in making decisions about true and false alarms. Our empirical results show that our technique is effective for real-world intrusion data.

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Correspondence to Nashat Mansour.

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Mansour, N., Chehab, M.I. & Faour, A. Filtering intrusion detection alarms. Cluster Comput 13, 19–29 (2010). https://doi.org/10.1007/s10586-009-0096-9

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Keywords

  • Alarm filtering
  • Computer security
  • Growing hierarchical self-organizing map
  • Intrusion detection
  • Self-organizing map