TY - GEN
T1 - E-BIAS
T2 - 11th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2015
AU - Sohankar, Javad
AU - Sadeghi, Koosha
AU - Banerjee, Ayan
AU - Gupta, Sandeep
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/11/2
Y1 - 2015/11/2
N2 - Security systems using brain signals or Electroencephalography (EEG), is an emerging field of research. Brain signal characteristics such as chaotic nature and uniqueness, make it an appropriate information source to be used in security systems. In this paper, E-BIAS, a pervasive EEG-based security system with both identification and authentication functionalities is developed. The main challenges are: 1) accuracy, 2) timeliness, 3) energy efficiency, 4) usability, and 5) robustness. Therefore, we apply Machine Learning (ML) algorithms with low training times, multi-tier distributed computing architecture, and commercial single channel dry electrode wireless EEG headsets to respectively overcome the first four challenges. With only two minutes of training time and a simple rest task, the authentication and identification performance reaches 95% and 80%, respectively on 10 subjects. We finally test the robustness of our EEG-based seamless security system against three types of attacks: a) brain impersonation, b) database hacking, and c) communication snooping and discuss the system configurations which can avoid data leakage.
AB - Security systems using brain signals or Electroencephalography (EEG), is an emerging field of research. Brain signal characteristics such as chaotic nature and uniqueness, make it an appropriate information source to be used in security systems. In this paper, E-BIAS, a pervasive EEG-based security system with both identification and authentication functionalities is developed. The main challenges are: 1) accuracy, 2) timeliness, 3) energy efficiency, 4) usability, and 5) robustness. Therefore, we apply Machine Learning (ML) algorithms with low training times, multi-tier distributed computing architecture, and commercial single channel dry electrode wireless EEG headsets to respectively overcome the first four challenges. With only two minutes of training time and a simple rest task, the authentication and identification performance reaches 95% and 80%, respectively on 10 subjects. We finally test the robustness of our EEG-based seamless security system against three types of attacks: a) brain impersonation, b) database hacking, and c) communication snooping and discuss the system configurations which can avoid data leakage.
KW - Electroencephalogram
KW - Pervasive security systems
UR - http://www.scopus.com/inward/record.url?scp=84958535715&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958535715&partnerID=8YFLogxK
U2 - 10.1145/2815317.2815341
DO - 10.1145/2815317.2815341
M3 - Conference contribution
AN - SCOPUS:84958535715
T3 - Q2SWinet 2015 - Proceedings of the 11th ACM Symposium on QoS and Security for Wireless and Mobile Networks
SP - 165
EP - 172
BT - Q2SWinet 2015 - Proceedings of the 11th ACM Symposium on QoS and Security for Wireless and Mobile Networks
PB - Association for Computing Machinery, Inc
Y2 - 2 November 2015 through 6 November 2015
ER -