A Survey of Keystroke Dynamics Biometrics <s> Multiple Biometric Modality Fusion. <s> In traditional Point of Sales (POS) device, the legitimate user is mainly authenticated by PIN (Personal ::: Identification Number). PIN method has drawbacks like easy to be forgotten. Fingerprint technology, ::: as the most viable biometrics in the special and restricted hardware environment of POS device, has ::: been used to increase user convenience. However, due to inherent problems, fingerprint alone can ::: hardly replace PIN in many high secure applications. Although keystroke pattern as one kind of ::: biometrics is in research for years, no real satisfied results have been got due to its inherent great ::: variability. In this paper, we proposed an innovative method by adapting and increase keystroke ::: pattern technology to POS device and applications. Together with fingerprint system, the adapted ::: keystroke pattern recognition system constitute a multibiometric system, which helps achieve an ::: increase in performance, and provide anti-spoofing measures by making it difficult for an intruder to ::: spoof multiple biometric traits simultaneously. Since this approach is a combination of partially featurebased ::: and partially knowledge-based, it can be a more deployable alternative to strengthen existing ::: PIN and pure biometric authentication methods. Experimental results support that this approach can ::: be better trade-off of the security and user convenience. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Multiple Biometric Modality Fusion. <s> The analysis of keystroke dynamics (KD) is a developing biometric technique for user authentication. In computer security, its use is limited to some constraints such as longer typing and practice sessions. In this paper, a practical user authentication system is proposed that combines a conventional login/password method and a said biometric technique. The conventional password authentication method is enhanced through analysis of keystroke dynamics (KD) and click patterns (CP). In this way increased security level is achieved without using long and complicated passwords. For this, an application is developed to demonstrate the technique and the results are analyzed. user time signatures (TS) are identified after analyzing user KD and CP. Based on ability to follow their specific TS, users are categorized into beginner, standard and expert. At the time of login, the user inputs are matched with respective database records for authentication. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Multiple Biometric Modality Fusion. <s> We propose in this paper a low cost multimodal biometric system combining keystroke dynamics and 2D face recognition. The objective of the proposed system is to be used while keeping in mind: good performances, acceptability, and espect of privacy. Different fusion methods have been used (min, max, mul, svm, weighted sum configured with genetic algorithms, and, genetic programming) on the scores of three keystroke dynamics algorithms and two 2D face recognition ones. This multimodal biometric system improves the recognition rate in comparison with each individual method. On a chimeric database composed of 100 individuals, the best keystroke dynamics method obtains an EER of 8.77%, the best face recognition one has an EER of 6.38%, while the best proposed fusion system provides an EER of 2.22%. <s> BIB003
Keystroke dynamics may not be sufficient to be a sole authenticator due to the rather low accuracy as compared to established biometrics such as fingerprint and iris modality. Therefore, researchers have tried to combine multiple biometrics with the objective to make it harder for an intruder to spoof several biometric traits simultaneously. A multibiometrics application system has been proposed by BIB001 utilizing keystroke dynamics and fingerprint feature. Aside from a match fingerprint minutiae data, input pattern of PIN number must also correspond to a certain similarity, thus, doubling up the authentication criteria. On the other hand, BIB002 proposed the fusion of keystroke input and unique click pattern on a Knock Pad as authentication feature, which reduced the need of relying on long and complicated password. Experimental result by BIB003 suggested that the combination of keystroke dynamics and face recognition was able to obtain better result than employing each trait independently.