Providing SIEM systems with self-adaptation
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- @Article{SuarezTangil:2013:IF,
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author = "Guillermo Suarez-Tangil and Esther Palomar and
Arturo Ribagorda and Ivan Sanz",
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title = "Providing {SIEM} systems with self-adaptation",
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journal = "Information Fusion",
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year = "2015",
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volume = "21",
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month = jan,
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pages = "145--158",
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keywords = "genetic algorithms, genetic programming, SIEM, Event
correlation, Artificial neural networks, Adaptive
system",
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ISSN = "1566-2535",
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DOI = "doi:10.1016/j.inffus.2013.04.009",
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URL = "http://www.sciencedirect.com/science/article/pii/S1566253513000535",
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abstract = "Security information and event management (SIEM) is
considered to be a promising paradigm to reconcile
traditional intrusion detection processes along with
most recent advances on artificial intelligence
techniques in providing automatic and self-adaptive
systems. However, classic management-related flaws
still persist, e.g. the fusion of large amounts of
security events reported from many heterogeneous
systems, whilst novel intriguing challenges arise
specially when dealing with the adaptation to newly
encountered and multi-step attacks. In this article, we
provide SIEM correlation with self-adaptation
capabilities to optimise and significantly reduce the
intervention of operators. In particular, our enhanced
correlation engine automatically learns and produces
correlation rules based on the context for different
types of multi-step attacks using genetic programming.
The context is considered as the knowledge and
reasoning, not only acquired by a human expert but also
inferred by our system, which assist in the
identification and fusion of events. In this regard, a
number of artificial neural networks are trained to
classify events according to the corresponding context
established for the attack. Experimentation is
conducted on a real deployment within OSSIM to validate
our proposal.",
- }
Genetic Programming entries for
Guillermo Nicolas Suarez de Tangil Rotaeche
Esther Palomar Gonzalez
D Arturo Ribagorda Garnacho
Ivan Sanz
Citations