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

Abstract : Network traffic monitoring is primordial for network operations and management for many purposes such as Quality-of-Service or security. One major difficulty when dealing with network traffic data (packets, flows...) is the poor semantic of individual attributes (number of bytes, packets, IP addresses, protocol, TCP/UDP port number...). Many attributes 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 network port 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 semantic 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.
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https://hal.inria.fr/hal-02345457
Contributor : Jérôme François <>
Submitted on : Monday, November 4, 2019 - 3:12:26 PM
Last modification on : Monday, November 30, 2020 - 10:26:03 PM
Long-term archiving on: : Wednesday, February 5, 2020 - 11:02:17 PM

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Laurent Evrard, Jérôme François, Jean-Noël Colin. Attacker Behavior-Based Metric for Security Monitoring Applied to Darknet Analysis. IM 2019 - The 16th IFIP/IEEE Symposium on Integrated Network and Service Management, Apr 2019, Washington DC, United States. ⟨hal-02345457⟩

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