TY - JOUR
T1 - Real-time electrocardiogram streams for continuous authentication
AU - Camara, Carmen
AU - Peris-Lopez, Pedro
AU - Gonzalez-Manzano, Lorena
AU - Tapiador, Juan
PY - 2018/7
Y1 - 2018/7
N2 - Security issues are becoming critical in modern smart systems.
Particularly, ensuring that only legitimate users get access to them is
essential. New access control systems must rely on continuous
authentication (CA) to provide higher security level. To achieve this,
recent research has shown how biological signals, such as
electroencephalograms (EEGs) or electrocardiograms (ECGs), can be useful
for this purpose. In this paper, we introduce a new CA scheme that,
contrarily to previous works in this area, considers ECG
signals as continuous data streams. The data stream paradigm is
suitable for this scenario since algorithms tailored for data streams
can cope with continuous data of a theoretical infinite length and with a
certain variability. The proposed ECG-based CA system is intended for
real-time applications and is able to offer an accuracy up to 96%, with
an almost perfect system performance (kappa statistic >80%).
AB - Security issues are becoming critical in modern smart systems.
Particularly, ensuring that only legitimate users get access to them is
essential. New access control systems must rely on continuous
authentication (CA) to provide higher security level. To achieve this,
recent research has shown how biological signals, such as
electroencephalograms (EEGs) or electrocardiograms (ECGs), can be useful
for this purpose. In this paper, we introduce a new CA scheme that,
contrarily to previous works in this area, considers ECG
signals as continuous data streams. The data stream paradigm is
suitable for this scenario since algorithms tailored for data streams
can cope with continuous data of a theoretical infinite length and with a
certain variability. The proposed ECG-based CA system is intended for
real-time applications and is able to offer an accuracy up to 96%, with
an almost perfect system performance (kappa statistic >80%).
KW - Datastreams
KW - Electrocardiogram
KW - Healthcare
KW - Identification
UR - http://www.scopus.com/inward/record.url?scp=85027406599&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2017.07.032
DO - 10.1016/j.asoc.2017.07.032
M3 - Article
AN - SCOPUS:85027406599
VL - 68
SP - 784
EP - 794
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
ER -