Countering terrorism, protecting critical national infrastructure and infrastructure assets through the use of novel behavioral biometrics

Research output: Contribution to journalArticle

Abstract

Since 9/11, there has been an increased interest in the use of biometric technologies as a way to counter the threat of terrorism and to protect a nation's critical national infrastructure and infrastructure assets. Biometric features can be used to verify an individual's identity or in the identification of an individual. Such features can be divided into two types, namely physical and behavioral characteristics. Physical biometrics are concerned with direct measurements of an individual's body and would include fingerprints, facial geometry and iris patterns. In contrast, behavioral biometrics are concerned with the indirect measurements of an individual, namely their patterns of behavior such as gait, voice and keystroke dynamics. Thus, each individual has a set of unique behavioral biometric features, which they usually adhere to under normal conditions. Based on this concept it is then possible to create different behavioral biometric profiles for each individual that can distinguish one individual from another. In this paper, we propose a video-based security system using style of hand waving as a novel behavioral biometric feature for individual identification.
LanguageEnglish
Pages197-211
JournalBehavioral Sciences of Terrorism and Political Aggression
Volume8
Issue number3
Early online date3 Mar 2016
DOIs
Publication statusE-pub ahead of print - 3 Mar 2016

Fingerprint

Terrorism
Dermatoglyphics
Iris
Gait
terrorism
assets
Hand
infrastructure
Technology
biometrics
video
mathematics
threat

Keywords

  • Behavioral biometrics
  • biometrics
  • counterterrorism
  • critical national infrastructure
  • hand waving
  • infrastructure assets

Cite this

@article{7f9d5ac650a5401886e53e8ea27eb379,
title = "Countering terrorism, protecting critical national infrastructure and infrastructure assets through the use of novel behavioral biometrics",
abstract = "Since 9/11, there has been an increased interest in the use of biometric technologies as a way to counter the threat of terrorism and to protect a nation's critical national infrastructure and infrastructure assets. Biometric features can be used to verify an individual's identity or in the identification of an individual. Such features can be divided into two types, namely physical and behavioral characteristics. Physical biometrics are concerned with direct measurements of an individual's body and would include fingerprints, facial geometry and iris patterns. In contrast, behavioral biometrics are concerned with the indirect measurements of an individual, namely their patterns of behavior such as gait, voice and keystroke dynamics. Thus, each individual has a set of unique behavioral biometric features, which they usually adhere to under normal conditions. Based on this concept it is then possible to create different behavioral biometric profiles for each individual that can distinguish one individual from another. In this paper, we propose a video-based security system using style of hand waving as a novel behavioral biometric feature for individual identification.",
keywords = "Behavioral biometrics, biometrics, counterterrorism, critical national infrastructure, hand waving, infrastructure assets",
author = "Priyanka Chaurasiaa and Pratheepan Yogarajah and Joan Condell and Girijesh Prasad and David McIlhatton and Rachel Monaghan",
note = "Reference text: Augusto, J. C. (2005). Temporal reasoning for decision support in medicine. Artificial Intelligence in Medicine, 33(1), 1–24. Augusto, J. C., & McCullagh, P. (2007). Ambient intelligence: Concepts and Applications. Computer Science and Information Systems, 4(1), 1–28. Bertenthal, B. I., & Pinto, P. (1993). Complementary processes in the perception and production of human movements. In L. B. Smith & E. Thelen (Eds.), A dynamic systems approach to development: Applications (pp. 209–239). Cambridge, MA: MIT Press. Blakemore, B. (2012). Cyberspace, cyber crime and cyber terrorism. In I. Awan & B. Blakemore (Eds.), Policing cyber hate, cyber threats and cyber terrorism (pp. 5–20). Farnham: Ashgate. Bobick, A., & Davis, J. (2001). The recognition of human movement using temporal templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(3), 257–267. Bonneau, J., Herley, C., van Oorschot, P. C., & Stajano, F. (2012). The quest to replace passwords: A framework for comparative evaluation of web authentication schemes. Proceedings of the IEEE Symposium on Security and Privacy (pp. 553–567), San Francisco, CA, USA. doi:10.1109/SP.2012.44 Boulgouris, N. V., Hatzinakos, D., & Plataniotis, K. N. (2005). Gait recognition: A challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, 22(6), 78–90. Br{\"o}mme, A. (2003). A classification of biometric signatures. Proceedings of the International Conference on Multimedia and Expo (pp. 17–20), Baltimore, USA. oi:10.1109/ICME.2003.1221237 Cabinet Office. (2010). Strategic framework and policy statement on improving resilience of critical infrastructure to disruption from natural hazards. London: Author. CAIN. (2015). List of significant violent incidents. Retrieved from http://cain.ulst.ac.uk/issues/violence/majinc.htm Canny, J. (1986). A computational approach to edge detection. IEEE Transaction Pattern Analysis and Machine Intelligence (PAMI), 8(6), 679–698. Centre for the Protection of National Infrastructure. (2016). The national infrastructure. Retrieved from http://www.cpni.gov.uk/about/cni/ Chaurasia, P., Yogarajah, P., Condell, J., Prasad, G., McIlhatton, D., & Monaghan, R. (2015). Biometrics and counter-terrorism: The case of gait recognition. Behavioral Sciences of Terrorism and Political Aggression, 7(3), 210–226. Cornish, P., Livingstone, D., Clemente, D., & Yorke, C. (2011). Cyber security and the UK’s critical national infrastructure. London: Royal Institute of International Affairs. Department of Homeland Security. (2015). Critical infrastructure sectors. Retrieved from http://www. dhs.gov/critical-infrastructure-sectors Di Nardo, J. V. (2009). Biometric technologies: Functionality, emerging trends, and vulnerabilities. Journal of Applied Security Research, 4, 194–216. Ellis, P. D. (2014). Lone wolf terrorism and weapons of mass destruction: An examination of capabilities and countermeasures. Terrorism and Political Violence, 26(1), 211–225. Felzenszwalb, P. F. (2001). Learning models for object recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1056–1062), Kauai, Hawaii, USA. doi:10.1109/CVPR. 2001.990647 Ferris, J. (2014, February 19). Terrorist attack shows vulnerability in critical infrastructure. The Daily Signal. Retrieved from http://dailysignal.com/2014/02/19/terrorist-attack-shows-vulnerabilitycritical- infrastructure/ Gleick, P. H. (2006). Water and terrorism. Water Policy, 8, 481–503. Hern{\'a}ndez-Encuentra, E., Pousada, P., & G{\'o}mez-Z{\'u}{\~n}iga, B. (2009). ICT and older people: Beyond usability. Educational Gerontology, 35(3), 226–245. Jain, A. K., Bolle, R., & Pankanti, S. (Eds.). (1999). Biometrics: Personal identification in networked society. London: Kluwer Academic. Jain, A. K., Nandakumar, K., & Nagar, A. (2008). Biometric template security. EURASIP Journal on Advances in Signal Processing, 1–17. doi:10.1155/2008/579416 Leibe, B., Seemann, E., & Schiele, B. (2005). Pedestrian detection in crowded scenes. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 878–885). Washington, DC, USA. Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009). Handbook of fingerprint recognition (2nd ed.). London: Springer. Marasco, E., & Ross, A. (2014). A survey on anti-spoofing schemes for fingerprint recognition systems. ACM Computing Survey, 47(2), 1–36. Pankanti, S., & Jain, A. K. (2008). Beyond fingerprinting. Scientific American, 299(3), 79–81. Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local SVM approach. Proceedings of the ICPR (Vol. III, pp. 32–36), Cambridge, UK. The Scottish Government. (2011). Secure and resilient: A strategic framework for critical national infrastructure in Scotland. Edinburgh: Author. Vacca, J. R. (2007). Biometric technologies and verification systems. Oxford: Elsevier Science & Technology. Wang, L., & Geng, X. (Eds.). (2010). Behavioral biometrics for human identification: Intelligent applications. Hershey, PA: IGI Global. Yogarajah, P., Condell, J. V., & Prasad, G. (2010) Individual identification from video based on ‘behavioural biometrics’. In L. Wang & X. Geng (Eds.), Behavioral biometrics for human identification: Intelligent applications (pp. 75–101). Hershey, PA: IGI Global. Yogarajah, P., Prasad, G., & Condell, J. V. (2009). Style of action based individual recognition in video sequences. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC, pp. 1237–1242), Singapore. Young, C. S. (2015). The science and technology of counterterrorism: Measuring physical and electronic security risk. London: Butterworth-Heinemann. Zegart, A. (2015). Threats within and without insider threats and organizational root causes: The 2009 fort hood terrorist attack. Parameters, 42(2), 35–46.",
year = "2016",
month = "3",
day = "3",
doi = "10.1080/19434472.2016.1146788",
language = "English",
volume = "8",
pages = "197--211",
journal = "Behavioral Sciences of Terrorism and Political Aggression",
issn = "1943-4472",
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TY - JOUR

T1 - Countering terrorism, protecting critical national infrastructure and infrastructure assets through the use of novel behavioral biometrics

AU - Chaurasiaa, Priyanka

AU - Yogarajah, Pratheepan

AU - Condell, Joan

AU - Prasad, Girijesh

AU - McIlhatton, David

AU - Monaghan, Rachel

N1 - Reference text: Augusto, J. C. (2005). Temporal reasoning for decision support in medicine. Artificial Intelligence in Medicine, 33(1), 1–24. Augusto, J. C., & McCullagh, P. (2007). Ambient intelligence: Concepts and Applications. Computer Science and Information Systems, 4(1), 1–28. Bertenthal, B. I., & Pinto, P. (1993). Complementary processes in the perception and production of human movements. In L. B. Smith & E. Thelen (Eds.), A dynamic systems approach to development: Applications (pp. 209–239). Cambridge, MA: MIT Press. Blakemore, B. (2012). Cyberspace, cyber crime and cyber terrorism. In I. Awan & B. Blakemore (Eds.), Policing cyber hate, cyber threats and cyber terrorism (pp. 5–20). Farnham: Ashgate. Bobick, A., & Davis, J. (2001). The recognition of human movement using temporal templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(3), 257–267. Bonneau, J., Herley, C., van Oorschot, P. C., & Stajano, F. (2012). The quest to replace passwords: A framework for comparative evaluation of web authentication schemes. Proceedings of the IEEE Symposium on Security and Privacy (pp. 553–567), San Francisco, CA, USA. doi:10.1109/SP.2012.44 Boulgouris, N. V., Hatzinakos, D., & Plataniotis, K. N. (2005). Gait recognition: A challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, 22(6), 78–90. Brömme, A. (2003). A classification of biometric signatures. Proceedings of the International Conference on Multimedia and Expo (pp. 17–20), Baltimore, USA. oi:10.1109/ICME.2003.1221237 Cabinet Office. (2010). Strategic framework and policy statement on improving resilience of critical infrastructure to disruption from natural hazards. London: Author. CAIN. (2015). List of significant violent incidents. Retrieved from http://cain.ulst.ac.uk/issues/violence/majinc.htm Canny, J. (1986). A computational approach to edge detection. IEEE Transaction Pattern Analysis and Machine Intelligence (PAMI), 8(6), 679–698. Centre for the Protection of National Infrastructure. (2016). The national infrastructure. Retrieved from http://www.cpni.gov.uk/about/cni/ Chaurasia, P., Yogarajah, P., Condell, J., Prasad, G., McIlhatton, D., & Monaghan, R. (2015). Biometrics and counter-terrorism: The case of gait recognition. Behavioral Sciences of Terrorism and Political Aggression, 7(3), 210–226. Cornish, P., Livingstone, D., Clemente, D., & Yorke, C. (2011). Cyber security and the UK’s critical national infrastructure. London: Royal Institute of International Affairs. Department of Homeland Security. (2015). Critical infrastructure sectors. Retrieved from http://www. dhs.gov/critical-infrastructure-sectors Di Nardo, J. V. (2009). Biometric technologies: Functionality, emerging trends, and vulnerabilities. Journal of Applied Security Research, 4, 194–216. Ellis, P. D. (2014). Lone wolf terrorism and weapons of mass destruction: An examination of capabilities and countermeasures. Terrorism and Political Violence, 26(1), 211–225. Felzenszwalb, P. F. (2001). Learning models for object recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1056–1062), Kauai, Hawaii, USA. doi:10.1109/CVPR. 2001.990647 Ferris, J. (2014, February 19). Terrorist attack shows vulnerability in critical infrastructure. The Daily Signal. Retrieved from http://dailysignal.com/2014/02/19/terrorist-attack-shows-vulnerabilitycritical- infrastructure/ Gleick, P. H. (2006). Water and terrorism. Water Policy, 8, 481–503. Hernández-Encuentra, E., Pousada, P., & Gómez-Zúñiga, B. (2009). ICT and older people: Beyond usability. Educational Gerontology, 35(3), 226–245. Jain, A. K., Bolle, R., & Pankanti, S. (Eds.). (1999). Biometrics: Personal identification in networked society. London: Kluwer Academic. Jain, A. K., Nandakumar, K., & Nagar, A. (2008). Biometric template security. EURASIP Journal on Advances in Signal Processing, 1–17. doi:10.1155/2008/579416 Leibe, B., Seemann, E., & Schiele, B. (2005). Pedestrian detection in crowded scenes. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 878–885). Washington, DC, USA. Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009). Handbook of fingerprint recognition (2nd ed.). London: Springer. Marasco, E., & Ross, A. (2014). A survey on anti-spoofing schemes for fingerprint recognition systems. ACM Computing Survey, 47(2), 1–36. Pankanti, S., & Jain, A. K. (2008). Beyond fingerprinting. Scientific American, 299(3), 79–81. Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local SVM approach. Proceedings of the ICPR (Vol. III, pp. 32–36), Cambridge, UK. The Scottish Government. (2011). Secure and resilient: A strategic framework for critical national infrastructure in Scotland. Edinburgh: Author. Vacca, J. R. (2007). Biometric technologies and verification systems. Oxford: Elsevier Science & Technology. Wang, L., & Geng, X. (Eds.). (2010). Behavioral biometrics for human identification: Intelligent applications. Hershey, PA: IGI Global. Yogarajah, P., Condell, J. V., & Prasad, G. (2010) Individual identification from video based on ‘behavioural biometrics’. In L. Wang & X. Geng (Eds.), Behavioral biometrics for human identification: Intelligent applications (pp. 75–101). Hershey, PA: IGI Global. Yogarajah, P., Prasad, G., & Condell, J. V. (2009). Style of action based individual recognition in video sequences. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC, pp. 1237–1242), Singapore. Young, C. S. (2015). The science and technology of counterterrorism: Measuring physical and electronic security risk. London: Butterworth-Heinemann. Zegart, A. (2015). Threats within and without insider threats and organizational root causes: The 2009 fort hood terrorist attack. Parameters, 42(2), 35–46.

PY - 2016/3/3

Y1 - 2016/3/3

N2 - Since 9/11, there has been an increased interest in the use of biometric technologies as a way to counter the threat of terrorism and to protect a nation's critical national infrastructure and infrastructure assets. Biometric features can be used to verify an individual's identity or in the identification of an individual. Such features can be divided into two types, namely physical and behavioral characteristics. Physical biometrics are concerned with direct measurements of an individual's body and would include fingerprints, facial geometry and iris patterns. In contrast, behavioral biometrics are concerned with the indirect measurements of an individual, namely their patterns of behavior such as gait, voice and keystroke dynamics. Thus, each individual has a set of unique behavioral biometric features, which they usually adhere to under normal conditions. Based on this concept it is then possible to create different behavioral biometric profiles for each individual that can distinguish one individual from another. In this paper, we propose a video-based security system using style of hand waving as a novel behavioral biometric feature for individual identification.

AB - Since 9/11, there has been an increased interest in the use of biometric technologies as a way to counter the threat of terrorism and to protect a nation's critical national infrastructure and infrastructure assets. Biometric features can be used to verify an individual's identity or in the identification of an individual. Such features can be divided into two types, namely physical and behavioral characteristics. Physical biometrics are concerned with direct measurements of an individual's body and would include fingerprints, facial geometry and iris patterns. In contrast, behavioral biometrics are concerned with the indirect measurements of an individual, namely their patterns of behavior such as gait, voice and keystroke dynamics. Thus, each individual has a set of unique behavioral biometric features, which they usually adhere to under normal conditions. Based on this concept it is then possible to create different behavioral biometric profiles for each individual that can distinguish one individual from another. In this paper, we propose a video-based security system using style of hand waving as a novel behavioral biometric feature for individual identification.

KW - Behavioral biometrics

KW - biometrics

KW - counterterrorism

KW - critical national infrastructure

KW - hand waving

KW - infrastructure assets

U2 - 10.1080/19434472.2016.1146788

DO - 10.1080/19434472.2016.1146788

M3 - Article

VL - 8

SP - 197

EP - 211

JO - Behavioral Sciences of Terrorism and Political Aggression

T2 - Behavioral Sciences of Terrorism and Political Aggression

JF - Behavioral Sciences of Terrorism and Political Aggression

SN - 1943-4472

IS - 3

ER -