I can be You: Questioning the use of Keystroke Dynamics as Biometrics Tey Chee Meng, Payas Gupta, Debin Gao Ke Chen

Outline Introduction Keystroke biometrics Experimental Design Experimental Results Conclusion

Authentication using Biometrics Physiological biometric: – facial features – hand geometry – Fingerprints – iris scans Behavioral biometric: – Signatures – Handwriting – Typing patterns （ i.e. keystroke dynamics ）

Is Keystroke Biometrics Unique? If imitation is possible, then keystroke dynamics would be unsuitable for use as a biometrics feature. it is possible to imitate someone else’s keystroke typing if appropriate feedback is provided?

Keystroke Dynamics Keystroke dynamics refer to information about the typing pattern. pressing and releasing of a keystroke pair (ka, kb) results in 4 timings which are of interest to keystroke biometrics systems

Keystroke Dynamics Key-down time: Key-up time: four relative timings can be derived:

Data vectorization

Anomaly Detector Scoring mean vector

Anomaly Detector Scoring absolute deviation vector

Anomaly Detector Scoring Euclidean distance based anomaly score Manhattan distance based anomaly score

Anomaly Detection Threshold FRR: false rejection rate, decrease as threshold sets higher FAR: false acceptance rate, increase as threshold sets higher EER: equal error rate where FRR=FAR

Experiment Design Attack scenarios – the attacker is able to extract the victim pattern from a compromised biometrics database. – the attacker may be able to capture samples of the victim’s keystrokes as she is authenticating (e.g. by installing a key- logger).

Choice of Password “serndele” – minimize finger movements on a standard US keyboard. “ths.ouR2” – chosen to maximize finger movements and therefore difficulty of typing.

Experiment 1 (e1) Training Data Collection 88 participants were asked to submit 200 samples for each of the two passwords using an existing keystroke dynamics based authentication system.

Experiment 2 (e2) Imitation using Euclidean distance 30 minutes imitation task: 84 participants played the role of attackers. 10 victims were randomly chosen from e1. Each attacker was randomly assigned one of the 10 victims, and was given the victim’s mean vector for. Attackers gets real-time feedback of the Euclidean distance based anomaly score.

Experiment 3 (e3a) Investigate the additional imitation session with Euclidean distance 14 best attackers were chosen from e2 to perform the same imitation task in e2 for only 20 minutes.

Experiment 4 (e3b) Investigate the imitation performance of highly motivated attackers in optimal environment Feedback is based on full victim typing pattern Information (Manhattan distance and absolute deviation)

Feedback Interface: Mimesis

Experiment Results Result from e1: collision attack given a target organization with 10 high value targets, if a team of 84 attackers were to be assembled, we expect to find on average, one attacker with the same typing pattern as one of the high value targets.

Experiment Results Results from e2: Improvement in FAR after imitation training

Experiment Results Results from e2: Effect of password difficulty The differences in mean between the easier and the harder password suggest that passwords that are easier to type are also easier to imitate.

Experiment Results Results from e2: effect of training duration 56% attackers took no more than 20 minutes to reach their b20 performance.

Experiment Results Results from e3a: – 6 attackers improved their b20 FAR – 4 attackers unchanged – 4 attackers worsened

Experiment Results Results from e3b:

Experiment Results Factors affecting imitation outcome – Gender: male performs significantly better than females – Therefore there exists a weak correlation between the imitation outcome and the similarity between the attacker and victim’s typing pattern – Typing speed, keyboard, Number of trials per minute are not affecting factors

Conclusion A user’s typing pattern can be imitated – Trained with incomplete model of the victim’s typing pattern, an attacker’s success rate is around 0.52 – The best attacker increases FAR to 1 after training – When the number of attackers and victims are sizeable, chance of natural collision is significant

Conclusion Easier passwords are easily imitated Males are better imitators

Questions?