Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics

Figures & data

Figure 1. Security breaches affecting 500 or more individuals (U.S. Secretary of HHS 2015).

Figure 2. General scheme of the EHR protection system.

Figure 3. In a distributed IDS, user profiles are stored in a central database on a server.

Figure 4. Fragment of a given user’s dataset with activity records.

Table 1. The principle of dividing the keys.

Category of keys	Keys	Key identifier ωj
Left function keys	Esc, F1… F7,	L1 L14
	Tab, Caps lock,
	Left shift, Left ctrl, Windows, Left alt
Right function keys	F8 … F12,	R1 R25
	PrtScr, Scroll lock, Pause, Insert, Delete, Home, End, PgUp, PgDown, NumLck, Backspace, Enter,
	Right Shift, Right Ctrl,
	Context, Right Alt,
	Arrows (up, down, left, right)
Alphanumeric keys	Letters and digits	ID1 ID64
Other keys	Keys not mentioned above

Figure 5. Tree structures for organizing the groups of keys and key pairs.

Figure 6. The general scheme of the proposed classification module.

Figure 7. Influence of g^uid parameter on accuracy level for exemplary user^A, user^B, user^C and user^D.

Table 2. Single classifiers giving the highest accuracy.

Classifier	Accuracy [%]
RF	89.81 ± 0.77
BN	86.99 ± 0.91
C4.5	86.74 ± 1.10
SVM	84.56 ± 0.57
Random Tree	79.24 ± 1.03
Ridor	78.58 ± 1.10

Table 3. The accuracy results for proposed classification module.

Ensembles of classifiers	Accuracy [%]
Single ensemble of classifiers (EC1)	96.58 ± 0.67
Classification module including all ensembles EC1,EC2, EC3 (see Figure 6)	98.08 ± 0.83
Classification module including five ensembles (EC1,…,EC5)	98.02 ± 0.85
Classification module including seven ensembles (EC1,…,EC7)	98.08 ± 0.72

Figure 8. Box-plot distributions of classification accuracy.

Table 4. A comparison of intrusion detection methods.

Method	Accuracy [%]	EER [%]	Data type ^1)
Our method (PSO for each user)	98.08	1.92	Continuous activity
Our method (PSO for all users)	97.83	2.17	Continuous activity
Lopatka et al. (Lopatka and Peetz 2009)	84.00	20.00	Long-text (free text, not continuous)
Tappert et al. identification, optimal conditions (Tappert, Villani, and Cha 2010)	97.40	*	Long-text (retyping, 650 characters, not continuous)
Tappert et al. identification, optimal conditions (Tappert, Villani, and Cha 2010)	95.60	*	Long-text (free text, 650 characters, not continuous)
Tappert et al. identification, nonoptimal conditions (Tappert, Villani, and Cha 2010)	60.75	*	Long-text (free text, 650 characters, not continuous)
Tappert et al. verification (Tappert, Villani, and Cha 2010)	90.20–95.6	*	Long-text (free text, 650 characters, not continuous)
Wesolowski et al. (Wesołowski and Porwik 2015)	*	10.59	Continuous activity
Zhong et al. (Zhong, Deng, and Jain 2012)	*	8.40	Repeated short sequence (password)
Pleva et al. (Pleva et al. 2015)	97.03	*	Repeated short sequence (password)
Monaco et al. (Monaco et al. 2013)	91.50	*	Long-text (free text, not continuous)

¹⁾ continuous activity—analysis of user’s continuous activity while performing everyday tasks, long text users could type a text freely or retype a given text, acquisition limited by time of writing or by length of the input text, repeated short sequence a single word (password) repeated many times.

* results are not available

Lopatka, M., and M. Peetz. 2009. Vibration sensitive keystroke analysis. Paper presented at Proceedings of the 18th Annual Belgian-Dutch Conference on Machine Learning, Tilburg, Netherlands, May 18–19, pp. 75–80.

 Google Scholar

Tappert, C. C., M. Villani, and S.-H. Cha. 2010. Keystroke biometric identification and authentication on long-text input. Behavioral Biometrics for Human Identification: Intelligent Applications 342–367. doi:10.4018/978-1-60566-725-6.ch016.

 Google Scholar

Wesołowski, T. E., and P. Porwik. 2015. Keystroke data classification for computer user profiling and verification. In Computational collective intelligence, M. Núñez, T. N. Nguyen, D. Camacho, and B. Trawiński, 588–597, Lecture Notes in Artificial Intelligence 9330. Springer International. 10.1007/978-3-319-24306-1_57

 Google Scholar

Zhong, Y., Y. Deng, and A. K. Jain. 2012. Keystroke dynamics for user authentication. In 2012 IEEE computer society conference on computer vision and pattern recognition workshops, 117–123. Washington, DC: IEEE Computer Society. doi:10.1109/CVPRW.2012.6239225.

 Google Scholar

Pleva, M., E. Kiktova, J. Juhar, and P. Bours. 2015. Acoustical user identification based on MFCC analysis of keystrokes. Advances in Electrical and Electronic Engineering 13 (4):309–313. doi:10.15598/aeee.v13i4.1466.

 Google Scholar

Monaco, J. V., J. C. Stewart, S. H. Cha, and C. C. Tappert. 2013. Behavioral biometric verification of student identity in online course assessment and authentication of authors in literary works. In biometrics: Theory, applications and systems (BTAS), 2013 IEEE sixth international conference on, 1–8. IEEE.doi:10.1109/BTAS.2013.6712743.

 Google Scholar