International audience

Keystroke dynamics can be useful to ascertain personal identity even after an authentication phase has been passed, provided that we are able to deal with the typing rhythms of free text, chosen and entered by users without any specific constraint. In this paper we present a method to compare typing samples of free text that can be used to verify personal identity. We have tested our technique with a wide set of experiments on 205 individuals, obtaining a False Alarm Rate of less than 5% and an Impostor Pass Rate of less than 0.005%. Different trade-offs are, however, possible. Our approach can rely on what is typed by people because of their normal job, and a few lines of text, even collected in different working sessions, are sufficient to reach a high level of accuracy, which improves proportionally to the amount of available information: As a consequence, we argue that our method can be useful in computer security as a complementary or alternative way to user authentication and as an aid to intrusion detection.

Unlike other access control systems based on biometric features, keystroke analysis has not led to techniques providing an acceptable level of accuracy. The reason is probably the intrinsic variability of typing dynamics, versus other---very stable---biometric characteristics, such as face or fingerprint patterns. In this paper we present an original measure for keystroke dynamics that limits the instability of this biometric feature. We have tested our approach on 154 individuals, achieving a False Alarm Rate of about 4% and an Impostor Pass Rate of less than 0.01%. This performance is reached using the same sampling text for all the individuals, allowing typing errors, without any specific tailoring of the authentication system with respect to the available set of typing samples and users, and collecting the samples over a 28.8-Kbaud remote modem connection.

Given a program P and a set of alternative programs //', we generate a sequence of test cases that are adequate, in the sense that they distinguish the given program from all alternatives The, m(,thod is related to fault-based approaches to test case generation, but programs in P need not he s]mp]e mutations of P. The technique for generating an adequate test set is based on the inductive learning of programs from finite sets of input-output examples: given a partial test set.we generate inductively a program P' E P which is consistent with P on those input values; then we look for an input value that distinguishes P from P', and we repeat the process until no program except P can be induced from the generated examples.We show that the obtained test set is adequate with respect to the alternatives belonging to P. The method IS made possible by a program induction procedure which has evolved from recent research in mnchine Iei]rnlng and inductive logic programming.An implemented version of the test case ~encration procedure is demonstrated on simple and more complex list-processing programs.and tb(, scalability of' the approach is discussed, ('ate~ories and Subject Descriptors: D.2.

As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.