Abstract
Demographic information has a rich context from which to make decisions about how to filter or individualize computer users in forensic analysis. Although current explorations into technologies such as face and fingerprint analysis have seen varying rates of success, two main problems limit their applicability in the context of computer crimes: they can be intrusive, and they can require costly equipment. Our solution is to determine users’ demographic traits by analyzing the interactions between users and computers. We conducted a field study that gathered users’ keystroke and mouse data during interaction with a computer. From user interaction data, we extracted keystroke timing and mouse movement features, and developed weighted random forest classifiers for five demographic traits: gender, age, ethnicity, handedness, and language. Experiments showed that these demographics can be accurately inferred from user interaction behavior, with recognition rates expressed by the area under the ROC curve (AUC) ranging from 82.11 % to 87.32 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)
Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)
Makinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)
Lapedriza, A., Marin-Jimenez, M.J., Vitria, J.: Gender recognition in non controlled environments. In: Proceedings of the International Conference on Robotics and Automation, pp. 834–837 (2006)
Wang, J., Lin, C., Chang, Y., Nagurka, M., Yen, C., Yeh, C.: Gender determination using fingertip features. Int. J. Med. Update 3(2), 22–28 (2008)
Badawi, A., Mahfouz, M., Tadross, R., Jantz, R.: Fingerprint-based gender classification. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition, June 2006
Wu, K., Childers, D.G.: Gender recognition from speech. Part I: coarse analysis. J. Acoust. Soc. Am. 90(4), 1828–1840 (1991)
Bocklet, T., Maier, A., Bauer, J.G., Burkhardt, F., Noth, E.: Age and gender recognition for telephone applications based on gmm supervectors and support vector machines. In: Proceedings of the IEEE ICASSP, pp. 1605–1608 (2008)
Li, X., Maybank, S.J., Yan, S., Tao, D., Xu, D.: Gait Components and their application to gender recognition. IEEE Trans. Syst., Man Cybern. C, Appl. Rev. 38(2), 145–155 (2008)
Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Process. 18(8), 1905–1910 (2009)
Lu, J., Tan, Y.P.: Gait-based human age estimation. IEEE Trans. Inf. Forensics Secur. 5(4), 761–770 (2010)
Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication. Future Generations Comput. Syst. 16(4), 351–359 (2000)
Bergadano, F., Gunetti, D., Picardi, C.: User authentication through keystroke dynamics. ACM Trans. Inf. Syst. Secur. 5(4), 367–397 (2002)
Ahmed, A.A.E., Traore, I.: A new biometric technology based on mouse dynamics. IEEE Trans. Depend. Secure Comput. 4(3), 165–179 (2007)
Shen, C., Cai, Z., Guan, X., Du, Y., Maxion, R.: User authentication through mouse dynamics. IEEE Trans. Inf. Forensics Secur. 8(1), 16–30 (2013)
Admit One Security, AdmitOneSecurity, http://www.biopassword.com
Behavio Sec, BehavioSec, http://www.behaviosec.com
Killourhy, K.S., Maxion, R.A.: Comparing anomaly detectors for keystroke dynamics. In: Proceedings of the 39th Annual International Conference on Dependable Systems and Networks, pp. 125–134 (2009)
Shen, C., Cai, Z.M., Maxion, R.A., Xiang, G., Guan, X.H.: Comparing classification algorithms for mouse dynamics based user identification. In: Proceedings of the 5th International Conference on Biometrics: Theory, Applications and Systems, pp. 61–66 (2012)
Jain, A.K., Dass, S.C., Nandakumar, K.: Can soft biometric traits assist user recognition? In: Proceedings of the SPIE. Biometric Technol. Human Identification, vol. 5404, pp. 561–572, (2004)
Giot, R., Rosenberger, C.: A new soft biometric approach for keystroke dynamics based on gender recognition. J. Inf. Tech. Manage. 11(1/2), 35–49 (2012)
Allen, W.H.: Computer forensics. IEEE Secur. Priv. 3(4), 59–62 (2005)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor. Newsl. 6(1), 1–6 (2004)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)
Acknowledgement
The research is supported by NFSC (61175039, 61221063), 863 High Tech Development Plan (2007AA01Z464, 2012AA011003), Research Fund for Doctoral Program of Higher Education of China (20090201120032), International Research Collaboration Project of Shaanxi Province (2013KW11), and Fundamental Research Funds for Central Universities (2012jdhz08). Roy Maxion was supported by the National Science Foundation, grant number CNS-0716677.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, C., Cai, Z., Maxion, R.A., Guan, X. (2014). On User Interaction Behavior as Evidence for Computer Forensic Analysis. In: Shi, Y., Kim, HJ., Pérez-González, F. (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science(), vol 8389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43886-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-662-43886-2_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43885-5
Online ISBN: 978-3-662-43886-2
eBook Packages: Computer ScienceComputer Science (R0)