Bours, Patrick

Hladek, Daniel

Hoff Skudal, Markus

Korecko, Stefan

Pleva, Matus

English

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Comparing Natural and Strong Typing Behaviorfor Keystroke Dynamics Multimodal DatabaseCollection?Markus Hoff Skudal1, Matus Pleva2[0000−0003−4380−0801],Štefan Korečko2[0000−0003−3647−6855], Patrick Bours1[0000−0001−5562−6957], andDaniel Hladek2[0000−0003−1148−3194]1 Norwegian University of Science and Technology,markuhs@stud.ntnu.no; patrick.bours@ntnu.no2 Technical University Kosice;{Matus.Pleva;Stefan.Korecko;daniel.hladek}@tuke.skAbstract. Multimodal Keystroke Dynamics has been accurately shownto increase identification and authentication precision. When expand-ing this technique with EMG (Electromyography) analysis, a concernarose regarding a suspected low dynamic range of the EMG data. Thusa small-scale, multimodal experiment with five subjects was performedto display its potential, both with natural and strong typing behavior.This experiment lays the groundwork for a future multimodal KeystrokeDynamics project with 50–100 subjects. The results suggest that naturaltyping behavior gives high quality, and possibly better, data than strongtyping. For 1-session-training, the evaluation shows user identificationaccuracy of 93% for natural and 85% for strong typing behavior. Hence,further research of scale could rely on natural typing behavior as thestandard approach of recording.Keywords: Keystroke Dynamics · User Identification · EMG · TimingAnalysis · Typing Behavior.1 IntroductionThe experiment presented in this poster captured multimodal data in the formof (1) Keyboard timing, (2) EMG signals from Myo3 armbands, (3) audio, and(4) video. More details on timing and audio keyboard analysis background can? This work has been supported by the Slovak Research and Development Agencyprojects APVV-16-0202 & APVV-15-0517, by Scientific Grant Agency of the Min-istry of Education, Science, Research and Sport of the Slovak Republic and theSlovak Academy of Sciences under the research projects VEGA 1/0753/20 & VEGA2/0165/21 and by Cultural and Educational Grant Agency of the Slovak Repub-lic grant Nos. KEGA 009TUKE-4/2019 & KEGA 035TUKE-4/2019, funded by theMinistry of Education, Science, Research and Sport of the Slovak Republic.3 https://developerblog.myo.com/2 M. Hoff Skudal, M. Pleva, Š. Korečko, P. Bours, D. Hladekbe found in [4]. From this data, timing and EMG data sets were studied todetermine typing behavior effectiveness. Recording of the Keystroke Dynamicsdata was exercised in four sessions for each of the five subjects, for each typingbehavior as described in [3]. This resulted in 20 session recordings for each formof typing. On average these sessions lasted 80 seconds and recorded 25 correcttypings of a predetermined word: ”password”. Ideally, the experiment shouldhave had a larger subject base. This, however, proved difficult due to the COVID-19 pandemic and local, physical restrictions.1.1 Keyboard Timing AnalysisIn Keyboard Timing Analysis we measure when each key is pressed down andwhen it is released. From that, we can calculate the time a key is held down(called the duration of that key) and the time elapsed between typing two con-secutive keys (called latency between the keys). Because the main goal of thispreliminary research was to test the capability to use EMG for identification, wehave only applied simple statistical analysis of the timing data, similar to whathas been done by Pleva et al. in [4]. Besides the Scaled Manhattan Distance(SMD) that was used in [4], we also implemented the Scaled Euclidean Distance(SED), as these are rather similar in their implementations.1.2 EMG Data AnalysisFor analysis, the EMG data was loaded from CSV files into a data-handlerfor easy access and manipulation. With one Myo armband on each arm, andeach armband producing eight EMG signals, each timestamp consisted of 16data points. The sampling rate was found to vary between 165–175 Hz, thoughit originally was set to 200Hz. In order to compare the two forms of typingbehavior, we chose MFCC (Mel-frequency cepstrum coefficients) [4, 5] with a 2-second window frame and 0.5-second step size to retrieve features. The MFCCwas performed on each signal individually, with a dynamically adjusted samplingrate, and then the coefficients were combined in a 1x208 dimension array foreach time frame (16 signals * 13 coefficients).In total, the experiment resultedin 2806 samples with 208 features, which stem from approximately 140 samplesfrom each subject per session.Despite our modest sample size, we hypothesized that the feature extrac-tion from the MFCC would provide pattern rich samples for neural networkanalysis. We experimented with four Keras4 models: GRU (Gated RecurrentUnits), LSTM (Long Short-Term Memory), CNN 1D (one-dimensional Convo-lution Network), and a basic Feed-Forward-Network for reference. The hypoth-esis was demonstrated as plausible, as all networks cross-validated performedabove 85%. The highest performing one-dimensional CNN-model was furtherused to compare natural and strong typing behavior. This model is composedof a Conv1D (32 nodes) and MaxPooling1D (5-sized kernel) layer, together with4 https://keras.io/Comparing Natural and Strong Typing Behavior for Keystroke Dynamics 3a Flatten, Dense (128 nodes), and Dense (5 nodes/classes, Softmax) layer. Thefollowing results are based on parameters batch size and epochs set to 64 and30 respectively.2 Results2.1 Keyboard Timing ResultsAs mentioned above, we have used the SMD and SED distance metrics to eval-uate the performance of the timing information. We have tested it on durationvalues only, latency values only, as well as the full set of duration and latencyvalues together. The results are given in Table 1.Table 1. Performance results from KD timing informationTyping Behavior: Natural StrongSMD SED SMD SEDDuration only 0.77 0.77 0.49 0.66Latency only 0.55 0.63 0.74 0.76All features 0.73 0.78 0.65 0.78The results in Table 1, even though only based on the typing behavior of 5persons, seem to indicate that duration values give a better performance whenusing natural typing, while latency features perform better for the strong typingbehavior. When using the combination of duration and latency features we seethat the natural typing behavior has a better performance when using SMDwhile for SED the performance is the same for both types of typing behavior.Based on the results of the typing behavior there is no clear advantage in col-lecting strong typing behavior samples in addition to the normal typing behaviorsamples.2.2 EMG Analysis ResultsEMG Data Characteristics. From the raw EMG data, we retrieved impor-tant characteristics to describe its properties and compared the data from nat-ural and strong typing behavior. Maximum and Minimum values were studied,together with the Median, Mean Absolute (MA = 1n∑nt=1 |xt|), and Root MeanSquared (RMS =√1n∑nt=1 x2t ) [1, 2]. It shows that on average both were higherfor strong typing behavior: MA 6.9 vs 7.4 and RMS 11.6 vs 12.4.Identification performance. When comparing the ability for identificationusing the best performed CNN 1D, both data sets provided above 85%. Whentraining on one session the Natural typing behavior scored a good 92.8% whilethe Strong typing behavior gave an accuracy of 85.2% (see Fig. 1).4 M. Hoff Skudal, M. Pleva, Š. Korečko, P. Bours, D. HladekFig. 1. The plot is the result of cross-validated session training on each type of typingbehavior. The average of identification accuracy for validation gave a 92.8% vs. 85.2%with 1-session-training.3 Discussion & ConclusionAs presented in this paper, the results suggest no clear benefit for further usageof Strong typing behavior in Multimodal Keystroke Dynamics Analysis. Theindications, however, are not entirely aligned in this view. While EMG sensoryidentification shows beneficial results for the exclusive use of Natural typingbehavior, the timing analysis is ambiguous. Based on these findings, we decidedto efficiently allocate resources to Natural typing behavior only. Hence, furtherresearch with Multimodal Keystrokes Dynamics on 50–100 subjects will utilizethis methodology in its recording.References1. Chong, H.J., Kim, S.J., Yoo, G.E.: Differential effects of type of keyboard play-ing task and tempo on surface EMG amplitudes of forearm muscles. Frontiers inPsychology 6, 1277 (2015). https://doi.org/10.3389/fpsyg.2015.012772. Grabczyński, A., Szklanny, K., Wrzeciono, P.: Applying EMG technology in medialand lateral elbow enthesopathy treatment using Myo motion controller. AustralasPhys Eng Sci Med. 42(3), 701–710 (2019), doi.org/10.1007/s13246-019-00770-53. Korečko, Š., Pleva, M., Haluška, M., Skudal, M.H., Bours, P.: EMG input data col-lection for multimodal keystroke analysis. In: 12th IEEE Int. Conference on Cogni-tive Infocommunications, CogInfoCom 2021 Sept. 23-25. pp. 205–210. IEEE (2021)4. Pleva, M., Bours, P., Ondáš, S., Juhár, J.: Improving static audio keystroke analysisby score fusion of acoustic and timing data. Multimedia Tools and Applications76(24), 25749–25766 (2017). https://doi.org/10.1007/s11042-017-4571-75. Rahim, M.A., Shin, J.: Hand movement activity-based character in-put system on a virtual keyboard. Electronics 9(5), 774 (May 2020).https://doi.org/10.3390/electronics9050774

Comparing Natural and Strong Typing Behavior for Keystroke Dynamics Multimodal Database Collection: Poster

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Comparing Natural and Strong Typing Behavior for Keystroke Dynamics Multimodal Database Collection: Poster

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