HUSÁK, Martin and Jaroslav KAŠPAR. Towards Predicting Cyber Attacks Using Information Exchange and Data Mining. Online. In Mobile Computing Conference (IWCMC). Limassol: IEEE, 2018, p. 536-541. ISBN 978-1-5386-2070-0.
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Basic information
Original name Towards Predicting Cyber Attacks Using Information Exchange and Data Mining
Authors HUSÁK, Martin (203 Czech Republic, guarantor, belonging to the institution) and Jaroslav KAŠPAR (203 Czech Republic, belonging to the institution).
Edition Limassol, Mobile Computing Conference (IWCMC), p. 536-541, 6 pp. 2018.
Publisher IEEE
Other information
Original language English
Type of outcome Proceedings paper
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/00216224:14610/18:00106887
Organization Ústav výpočetní techniky – Repository – Repository
ISBN 978-1-5386-2070-0
ISSN 2376-6492
UT WoS 000447259500091
Keywords in English attack prediction;collaborative security;information exchange;data mining
Links VI20162019029, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 5/9/2020 16:03.
Abstract
In this paper, we present an empirical evaluation of an approach to predict attacker's activities based on information exchange and data mining. We gathered the cyber security alerts shared within the SABU platform, in which around 220,000 alerts from heterogeneous geographically distributed sensors (intrusion detection systems and honeypots) are shared every day. Subsequently, we used the methods of sequential rule mining to identify common attack patterns and to derive rules for predicting attacks. As we illustrate in this paper, a collaborative environment allows attack prediction in multiple dimensions. First, we can predict what will the attacker do next and when. Second, we can predict where will the attack hit, e.g., when an attacker is targeting several networks at once. In a week-long experiment, we processed in total over 1 million alerts, from which we mined predictive rules every day. Our findings show that most of the rules display stable values of support and confidence and, thus, can be used to predict cyber attacks in consecutive days after mining without a need to actualize the rules every day.
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