El ecosistema informativo digital en Argentina

After obtaining the users’ raw data, extracting the keystroke features is performed [64]. These features are computed using two main values, specifically: the press time (Dn) and the release

time (Un) of each key (n), in milliseconds.

One keystroke feature is extracted from the timing of a single pressed key as shown in Figure 2.3. It had been used solely as a timing feature in [65] and coupled with other timing features in [45, 66, 67]. This feature is explained below:

‐ Duration time (also called dwell time or hold time) (H). It is the time a key is pressed until it is released. It can be computed using the following formula:

(2.1)

Three keystroke features are extracted from the timing of each di-graph, i.e. two successive keys, as shown in Figure 2.3. These features are called keystroke latencies or flight times. The three types of keystroke latencies are listed below:

‐ Down-down time (DD) (also called press-press time (PP)): It is the interval time between two successive key presses; used in studies such as [68, 8, 69]. It can be computed using the following formula:

(2.2)

‐ Up-up (UU) time (also called release-release time (RR)): is the interval time between two successive key releases; utilized in [70, 52]. It can be computed using the following formula:

‐ Up-down (UD) time (also called release-press time (RP) or inter-key time): is the interval time between a key release and the next key press; examples of studies exploiting it are [49, 71, 50] . Moreover, UD time can be a negative value in the case that the next key is pressed before releasing the previous one, which can happen in the case of very fast typists. It can be computed using the following formula:

(2.4)

Figure 2.3: Keystroke timing features.

Most studies employed more than one of these latencies at once [72, 52]. Moreover, some studies claim that using hold time yields better results [54], while others argue that latency features produce better results [69]. Nonetheless, many studies deduced that using combinations of both hold and latency times have the best effects on the system performance [66, 72].

Moreover, the down-down (DD) latency was utilized between more than two keys. Bergadano et al. [73] and Bond & Awad [74] employed it on tri-graphs, i.e. three keys; while Gunetti and Picardi [8] applied it to n-graphs, i.e. 4-graph, 5-graph and 6-graph. Using a larger ‘n’ increased the authentication performance based on the work done in [73].

Although, di-graph and tri-graph time has been used in plenty of research, Sim and Janakiraman [75] concluded from their several experiments that using di-graphs/tri-graphs is not a good discriminator between users when the actual typed words are not taken into consideration. This is because the context of the text that a particular letter is included in regulates the manner in which it is typed [44] i.e. the letters ‘te’ have different latency times in the words ‘sentence’ and ‘teacher’. Therefore, di-graphs/tri-graphs are more effective for keystroke dynamics when using context-specific n-graphs.

Hold UD UU DD  Hold  U1 D2 U2 D1  Key2  Time  Key1 

In many keystroke systems, users’ profiles consist of the mean and/or standard deviation of each key hold time and/or di-graph latency [33, 76]. Furthermore, only the latencies’ means and standard deviations for di-graphs that have occurred a minimum number of times are included in the users’ profiles in some studies such as [77, 78]. Moreover, only a fixed set of letters and two letter combinations were used in [44, 79]; these sets were chosen based on each letter’s frequency in the English language. Letters including E, A, T etc. and di-graphs including: AT, TH, HE etc. are frequently found in English text, therefore, it is good practice to use the mean and standard deviation of their duration and latencies in the user’s template, which will increase its stability.

Other less common features were considered as well. Typing speed or words-per-minute (WPM) was used in [70]; it is the number of words that a user types in a minute. Frequency of error was also used in [80]; it measures the number of times that the user makes use of the backspace key. Shift key usage was used as well in [57]; it calculates the percentage of using the right and left shift-keys. Moreover, the additional keys usage was used in [49]; it computes the frequency of using special keys such as the num-pad and the control keys. Then again, to use these features, a piece of text with considerable length has to be typed in order to correctly capture the user’s habits.

Another feature that was used for inferring the typing behaviour for users, was the typing pressure [81, 82]. This feature computes the pressure applied to each key being pressed on the keyboard, whilst the user is typing. This feature suffers from the drawback of having to use a special kind of keyboard, which defeats the purpose of using keystrokes in the first place.

Other features that also suffer from having to use additional hardware include the finger placement feature [83]. This feature analyses the positioning and the angle that the fingers are applying on each key being pressed. Even the finger choice for pressing each key on the keyboard is studied as a feature for keystroke dynamics [84]. Moreover, the shape and position of hands while typing were also used as features to infer users’ typing behaviour [85]. Unfortunately, all these features require an external camera to capture the shape, position of the hands and the placement, and choice of the fingers during the time of authentication.

Quite a recent study has investigated the use of keystroke intensity as a feature for user authentication [86]. Keystroke intensity overcomes the problem of typing pressure, hand

shape and position, finger placement and choice as it does not impose the need for an additional camera. It is captured using the PC’s built-in microphone which is used to acquire an audio recording of the user’s typing. Nevertheless, this feature enforces a semi-controlled environment in which the user has to be alone in a quiet room to carry-out the typing.

Table 2.2 lists all the keystroke features used for user authentication.

Table 2.2: Features of keystroke dynamics.

Category Features Conventional Features Hold time Down-Down time Up-Up time Down-Up time Non-conventional Features Typing speed Error rate Shift key usage Special keys usage

Additional Hardware Features

Typing pressure Fingers placement and choice

Hands shape and position Keystroke intensity