id: 05906177
dt: a
an: 05906177
au: Mora-Gimeno, Francisco José; Maciá-Pérez, Francisco; Lorenzo-Fonseca,
    Iren; Gil-Martínez-Abarca, Juan Antonio; Marcos-Jorquera, Diego;
    Gilart-Iglesias, Virgilio
ti: Security alert correlation using growing neural gas.
so: Herrero, Álvaro (ed.) et al., Computational intelligence in security for
    information systems. 4th international conference, CISIS 2011, held at
    IWANN 2011, Torremolinos-Málaga, Spain, June 8‒10, 2011.
    Proceedings. Berlin: Springer (ISBN 978-3-642-21322-9/pbk). Lecture
    Notes in Computer Science 6694, 76-83 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: Alert correlation; Neural networks; Intrusion detection; Growing neural gas
ci: 
li: doi:10.1007/978-3-642-21323-6_10
ab: Summary: The use of alert correlation methods in Distributed Intrusion
    Detection Systems (DIDS) has become an important process to address
    some of the current problems in this area. However, the efficiency
    obtained is far from optimal results. This paper presents a novel
    approach based on the integration of multiple correlation methods by
    using the neural network Growing Neural Gas (GNG). Moreover, since
    correlation systems have different detection capabilities, we have
    modified the learning algorithm to positively weight the best
    performing systems. The results show the validity of the proposal, both
    the multiple integration approach using GNG neural network and the
    weighting based on efficiency.
rv: