Abstract: The present invention provides a method of reducing the amount of memory required to scan a given data string for the presence of computer viruses or other data traits of interest including the steps of 1) loading into a memory of a computer a set of generic features that are functionally similar to standard computer virus signatures, but tend to be less specific to particular viruses, 2) locating occurrences of the generic features within the data string, 3) applying a first mapping from the occurrences located during step 2) to obtain a subset of standard signatures, 4) loading the subset of standard signatures into a memory of said computer, 5) locating occurrences within the data string of all signatures from the subset of standard signatures, and 6) applying a second mapping from the occurrences located during step 5) to identify a set of computer viruses that are likely to be present in the data string.

Abstract: A data string is a sequence of atomic units of data that represent information. In the context of computer data, examples of data strings include executable programs, data files, and boot records consisting of sequences of bytes, or text files consisting of sequences of bytes or characters. The invention solves the problem of automatically constructing a classifier of data strings, i.e., constructing a classifier which, given a string, determines which of two or more class labels should be assigned to it. From a set of (string, class-label) pairs, this invention provides an automated technique for extracting features of data strings that are relevant to the classification decision, and an automated technique for developing a classifier which uses those features to classify correctly the data strings in the original examples and, with high accuracy, classify correctly novel data strings not contained in the example set.

Abstract: A data string is a sequence of atomic units of data that represent information. In the context of computer data, examples of data strings include executable programs, data files, and boot records consisting of sequences of bytes, or text files consisting of sequences of bytes or characters. The invention solves the problem of automatically constructing a classifier of data strings, i.e., constructing a classifier which, given a string, determines which of two or more class labels should be assigned to it. From a set of (string, class-label) pairs, this invention provides an automated technique for extracting features of data strings that are relevant to the classification decision, and an automated technique for developing a classifier which uses those features to classify correctly the data strings in the original examples and, with high accuracy, classify correctly novel data strings not contained in the example set.