Detection of Botnet Command and Control Traffic by the Identification of Untrusted Destinations

Pieter Burghouwt; Marcel Spruit; Henk Sips

International Conference on Security and Privacy in Communication Networks. 10th International ICST Conference, SecureComm 2014, Beijing, China, September 24-26, 2014, Revised Selected Papers, Part I

Research Article

Detection of Botnet Command and Control Traffic by the Identification of Untrusted Destinations

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@INPROCEEDINGS{10.1007/978-3-319-23829-6_13,
    author={Pieter Burghouwt and Marcel Spruit and Henk Sips},
    title={Detection of Botnet Command and Control Traffic by the Identification of Untrusted Destinations},
    proceedings={International Conference on Security and Privacy in Communication Networks. 10th International ICST Conference, SecureComm 2014, Beijing, China, September 24-26, 2014, Revised Selected Papers, Part I},
    proceedings_a={SECURECOMM},
    year={2015},
    month={11},
    keywords={Botnets Network intrusion detection Anomaly detection},
    doi={10.1007/978-3-319-23829-6_13}
}

Pieter Burghouwt
Marcel Spruit
Henk Sips
Year: 2015
Detection of Botnet Command and Control Traffic by the Identification of Untrusted Destinations
SECURECOMM
Springer
DOI: 10.1007/978-3-319-23829-6_13

Pieter Burghouwt¹^,*, Marcel Spruit¹^,*, Henk Sips¹^,*

1: Delft University of Technology

*Contact email: P.Burghouwt@tudelft.nl, M.E.M.Spruit@hhs.nl, H.J.Sips@tudelft.nl

Abstract

We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.

Keywords: Botnets Network intrusion detection Anomaly detection

Published: 2015-11-23
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-23829-6_13