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Limitation of Honeypot/Honeynet Databases to Enhance Alert Correlation

  • Yosra Ben Mustapha
  • Hervé Débar
  • Grégoire Jacob
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7531)

Abstract

In SIEM environments, security analysts process massive amount of alerts often imprecise. Alert correlation has been designed to efficiently analyze this large volume of alerts. However, a major limitation of existing correlation techniques is that they focus on the local knowledge of alerts and ignore the global view of the threat landscape. In this paper, we introduce an alert enrichment strategy that aims at improving the local domain knowledge about the event with relevant global information about the threat in order to enhance the security event correlation process.

Today, the most prominent sources of information about the global threat landscape are the large honeypot/honeynet infrastructures which allow us to gather more in-depth insights on the modus operandi of attackers by looking at the threat dynamics. In this paper, we explore four honeypot databases that collect information about malware propagation and security information about web-based server profile. We evaluate the use of these databases to correlate local alerts with global knowledge. Our experiments show that the information stored in current honeypot databases suffers from several limitations related to: the interaction level of honeypots that influences their coverage and their analysis of the attacker’s activities, collection of raw data which may include imprecise or voluminous information, the lack of standardization in the information representation which hinder cross-references between different databases, the lack of documentation describing the available information.

Keywords

Intrusion Detection System Enrichment Process Attack Scenario Malicious Activity Global Threat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yosra Ben Mustapha
    • 1
  • Hervé Débar
    • 1
  • Grégoire Jacob
    • 1
  1. 1.Telecom Sudparis, SAMOVAR UMR 5157EvryFrance

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