Attacker Behavior-Based Metric for Security Monitoring Applied to Darknet Analysis

Laurent Evrard, Jérôme François, Jean-Noël Colin

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution


Network traffic monitoring is primordial for network operations and management including Quality-of-Service or security. One major difficulty when dealing with network traffic data (packets, flows, etc) is the poor semantic of individual attributes (number of bytes, packets, IP addresses, protocol, TCP/UDP port numbers, etc). Many of them can be represented as numerical values but cannot be mapped to a meaningful metric space. Most notably are application port numbers. They are numerical but comparing them as integers is meaningless. In this paper, we propose a fine grained attacker behavior-based similarity metric allowing traffic analysis to take into account semantic relations between port numbers. The behavior of attackers is derived from passive observation of a darknet or telescope, aggregated in a graph model, from which a dissimilarity function is defined. We demonstrate the veracity of this function with real world network data in order to pro-actively block 99% of TCP scans.

Original languageEnglish
Title of host publication2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
Number of pages9
ISBN (Electronic)9783903176157
Publication statusPublished - 16 May 2019
EventIFIP/IEEE International Symposium on Integrated Network Management -
Duration: 8 Apr 201912 Apr 2019


ConferenceIFIP/IEEE International Symposium on Integrated Network Management
Abbreviated titleIM


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