Abstract: A method of optimizing a manufacturing operation includes receiving as input data a description of a plurality of desired results of the operation, receiving as input data capabilities of a machine to accomplish the operation, determining an operation space including of a set of operations sufficient to accomplish all of the plurality of desired results using the machine capabilities, categorizing the operation space into groups, each group corresponding to one of the plurality of desired results, wherein any operation in the group sufficing to accomplish the corresponding one result, selecting a subset of the operation space, the subset comprising at least one representative from each of the groups, thereby making it sufficient to accomplish all of the plurality of desired results, and determining a short way to sequence the operations from the selected subset.

Abstract: A method of constructing data classifiers in cases where the number of features is much larger than the number of training examples and it is critical to avoid overtraining, is particularly useful in recognizing computer objects containing or likely to contain a computer virus. The method provides a novel way of tolerating imperfectly classifiable data, of learning in time polynomial in the size of the input data, and of avoiding overtraining. Training is performed by constructing and solving a linear program (LP) or integer program (IP).

Abstract: A method of optimizing a manufacturing operation includes receiving as input data a description of a plurality of desired results of the operation, receiving as input data capabilities of a machine to accomplish the operation, determining an operation space including of a set of operations sufficient to accomplish all of the plurality of desired results using the machine capabilities, categorizing the operation space into groups, each group corresponding to one of the plurality of desired results, wherein any operation in the group sufficing to accomplish the corresponding one result, selecting a subset of the operation space, the subset comprising at least one representative from each of the groups, thereby making it sufficient to accomplish all of the plurality of desired results, and determining a short way to sequence the operations from the selected subset.

Abstract: A system for continuous monitoring and autonomous detection of patterns in the main memory subsystem of a computer system. The invention can be embodied as an extension to existing memory scrubbing hardware to permit stored code pattern analysis and identification during the autonomous transparent memory scrubbing process. A library of stored target signatures is provided to which code signatures are compared during analysis. Code signatures may be derived directly from the memory subsystem data pattern or may be indirectly and more efficiently derived from the error correction code (ECC) string associated with the stored data pattern. This invention is directly applicable to computer virus detection and neutralization systems.

Abstract: The present invention concerns a method and system for protection against corruption of data. In order to backup files to be stored in a storage medium, a set of redundant parity symbols is computed by encoding cross-sections across said files using a systematic code. These parity symbols and the files are then stored for later retrieval. If some of these files are erased, corrupted, damaged or infected by a virus, they are reconstructed by decoding the surviving files and the parity symbols.

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 system and method for verifying the integrity of a computer system's BIOS programs stored in alterable read only memory (such as FLASH ROM), and preventing malicious alteration thereof. The system and method regularly check the contents of the alterable read only memory using a digital signature encrypted by means of an asymmetrical key cryptosystem.

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.