id: 05968424
dt: a
an: 05968424
au: Antunes, Mário; Correia, Manuel E.
ti: Tunable immune detectors for behaviour-based network intrusion detection.
so: Liò, Pietro (ed.) et al., Artificial immune systems. 10th international
    conference, ICARIS 2011, Cambridge, UK, July 18‒21, 2011.
    Proceedings. Berlin: Springer (ISBN 978-3-642-22370-9/pbk). Lecture
    Notes in Computer Science 6825, 334-347 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: Artificial Immune System; Tunable Activation Threshold; Network Intrusion
    Detection; Anomaly Detection
ci: 
li: doi:10.1007/978-3-642-22371-6_29
ab: Summary: Computer networks are highly dynamic environments in which the
    meaning of normal and anomalous behaviours can drift considerably
    throughout time. Behaviour-based Network Intrusion Detection System
    (NIDS) have thus to cope with the temporal normality drift intrinsic on
    computer networks, by tuning adaptively its level of response, in order
    to be able to distinguish harmful from harmless network traffic flows.
    In this paper we put forward the intrinsic Tunable Activation Threshold
    (TAT) theory ability to adaptively tolerate normal drifting network
    traffic flows. This is embodied on the TAT-NIDS, a TAT-based Artificial
    Immune System (AIS) we have developed for network intrusion detection.
    We describe the generic AIS framework we have developed to assemble
    TAT-NIDS and present the results obtained thus far on processing real
    network traffic data sets. We also compare the performance obtained by
    TAT-NIDS with the well known and widely deployed signature-based snort
    network intrusion detection system.
rv: