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Authors: Z. Tsiatsikas 1 ; A. Fakis 1 ; D. Papamartzivanos 1 ; D. Geneiatakis 2 ; G. Kambourakis 1 and C. Kolias 3

Affiliations: 1 University of the Aegean, Greece ; 2 Aristotle University of Thessaloniki, Greece ; 3 George Mason University, United States

ISBN: 978-989-758-117-5

Keyword(s): Session Initiation Protocol, Machine Learning, DDoS, Anomaly-detection, Intrusion Detection Systems.

Related Ontology Subjects/Areas/Topics: Information and Systems Security ; Intrusion Detection & Prevention ; Network Security ; Wireless Network Security

Abstract: This paper focuses on network anomaly-detection and especially the effectiveness of Machine Learning (ML) techniques in detecting Denial of Service (DoS) in SIP-based VoIP ecosystems. It is true that until now several works in the literature have been devoted to this topic, but only a small fraction of them have done so in an elaborate way. Even more, none of them takes into account high and low-rate Distributed DoS (DDoS) when assessing the efficacy of such techniques in SIP intrusion detection. To provide a more complete estimation of this potential, we conduct extensive experimentations involving 5 different classifiers and a plethora of realistically simulated attack scenarios representing a variety of (D)DoS incidents. Moreover, for DDoS ones, we compare our results with those produced by two other anomaly-based detection methods, namely Entropy and Hellinger Distance. Our results show that ML-powered detection scores a promising false alarm rate in the general case, and seems to outperform similar methods when it comes to DDoS. (More)

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Paper citation in several formats:
Tsiatsikas, Z.; Fakis, A.; Papamartzivanos, D.; Geneiatakis, D.; Kambourakis, G. and Kolias, C. (2015). Battling Against DDoS in SIP - Is Machine Learning-based Detection an Effective Weapon?.In Proceedings of the 12th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2015) ISBN 978-989-758-117-5, pages 301-308. DOI: 10.5220/0005549103010308

@conference{secrypt15,
author={Z. Tsiatsikas. and A. Fakis. and D. Papamartzivanos. and D. Geneiatakis. and G. Kambourakis. and C. Kolias.},
title={Battling Against DDoS in SIP - Is Machine Learning-based Detection an Effective Weapon?},
booktitle={Proceedings of the 12th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2015)},
year={2015},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005549103010308},
isbn={978-989-758-117-5},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2015)
TI - Battling Against DDoS in SIP - Is Machine Learning-based Detection an Effective Weapon?
SN - 978-989-758-117-5
AU - Tsiatsikas, Z.
AU - Fakis, A.
AU - Papamartzivanos, D.
AU - Geneiatakis, D.
AU - Kambourakis, G.
AU - Kolias, C.
PY - 2015
SP - 301
EP - 308
DO - 10.5220/0005549103010308

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