The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature's center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.
The article concerns the problem of detecting masqueraders in computer systems. A masquerader in a computer system is an intruder who pretends to be a legitimate user in order to gain access to protected resources. The article presents an intrusion detection method based on a fuzzy approach. Two types of user's activity profiles are proposed along with the corresponding data structures. The solution analyzes the activity of the computer user in a relatively short period of time, building a user's profile. The profile is based on the most recent activity of the user, therefore, it is named the local profile. Further analysis involves creating a more general structure based on a defined number of local profiles of one user, called the fuzzy profile. It represents a generalized behavior of the computer system user. The fuzzy profiles are used directly to detect abnormalities in users' behavior, and thus possible intrusions. The proposed solution is prepared to be able to create user's profiles based on any countable features derived from user's actions in computer system (i.e., used commands, mouse and keyboard data, requested network resources). The presented method was tested using one of the commonly available standard intrusion data sets containing command names executed by users of a Unix system. Therefore, the obtained results can be compared with other approaches. The results of the experiments Communicated by V. Loia. have shown that the method presented in this article is comparable with the best intrusion detection methods, tested with the same data set, in the matter of the obtained results. The proposed solution is characterized by a very low computational complexity, which has been confirmed by experimental results.
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