Abstract: A network device includes a hardware pipeline to process a network packet to be encrypted. A portion of the hardware pipeline retrieves information from the network packet and generates a command based on the information. A block cipher circuit is coupled inline within the hardware pipeline. The hardware pipeline includes hardware engines coupled between the portion of the hardware pipeline and the block cipher circuit. The hardware engines parse and execute the command to determine a set of inputs and input the set of inputs and portions of the network packet to the block cipher circuit. The block cipher circuit encrypts a payload data of the network packet based on the set of inputs.

Abstract: Provided herein are a method and device for detection of anomalous instructions sent from a controller of a medical device, to be received by a medical device. The method and the device utilize a dual layer architecture including a first, unsupervised detection layer and a second, supervised detection layer, wherein the layers are applied to the received instructions in series to efficiently detect anomalous instruction prior to the instructions reaching the medical device.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. According to the proposed method, hardware and/or software parameters that can characterize known behavioral patterns in the computerized system are determined. Known malicious code samples are learned by a machine learning process, such as decision trees, Naïve Bayes, Bayesian Networks, and artificial neural networks, and the results of the machine learning process are analyzed in respect to these behavioral patterns. Then, known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. Accordingly, hardware and/or software parameters are determined in the computerized system that is can characterize known behavioral patterns thereof. Known malicious code samples are learned by a machine learning process, such as decision trees and artificial neural networks, and the results of the machine learning process are analyzed in respect to the behavioral patterns of the computerized system. Then known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: Provided herein system and method for analyzing time oriented data in a plurality of records, by defining a knowledge base in a domain and linking it to a database of a plurality of records, each storing at least one instance of time oriented data based on at least one concept defined in the knowledge base; and specifying at least one constraint on the subject records; and retrieving subject records which satisfy at least one constraint and graphically displaying at least one instance of time oriented data stored in the retrieved subject records; and exploring at least one association between the instance of time oriented data stored in the retrieved records.

Abstract: The invention is a comprehensive conceptual and computational architecture that enables monitoring accumulated time-oriented data using knowledge related to the operation of elements of a computer network and deriving temporal abstractions from the accumulated data and the knowledge in order to identify electronic threat patterns and create alerts. The architecture of the invention supports two main modes of operation: a. an automated, continuous mode for monitoring, recognition and detection of known eThreats; and b. an interactive, human-operated intelligent tool for dynamic exploration of the contents of a security storage service to identify new temporal patterns that characterize such threats, and to add them to the monitoring database. The architecture of the invention can analyze data collected from various sources, such as end-user devices, network element, network links etc., to identify potentially infected devices, files, sub-streams or network segments.

Abstract: Method for enhancing the performance of a medical search engine, including the procedures of generating an inverted index of medical related documents, receiving a medical search query from a user, expanding and augmenting the received medical search query thereby generating an enhanced medical search query, retrieving all the medical related documents in the inverted index which are relevant to the enhanced medical search query, ranking the retrieved medical related documents according to a master expression, presenting the ranked retrieved medical related documents to the user, receiving at least one user feedback response from the user to a respective one of the ranked retrieved medical related documents, for each received user feedback response evaluating and storing at least one feature of the respective one of the ranked retrieved medical related documents and modifying the master expression based on the received user feedback response using at least one machine learning algorithm.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. According to the proposed method, hardware and/or software parameters that can characterize known behavioral patterns in the computerized system are determined. Known malicious code samples are learned by a machine learning process, such as decision trees, Naïve Bayes, Bayesian Networks, and artificial neural networks, and the results of the machine learning process are analyzed in respect to these behavioral patterns. Then, known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: Method for detecting malicious behavioral patterns which are related to malicious software such as a computer worm in computerized systems that include data exchange channels with other systems over a data network. Accordingly, hardware and/or software parameters are determined in the computerized system that is can characterize known behavioral patterns thereof. Known malicious code samples are learned by a machine learning process, such as decision trees and artificial neural networks, and the results of the machine learning process are analyzed in respect to the behavioral patterns of the computerized system. Then known and unknown malicious code samples are identified according to the results of the machine learning process.

Abstract: The invention is a comprehensive conceptual and computational architecture that enables monitoring accumulated time-oriented data using knowledge related to the operation of elements of a computer network and deriving temporal abstractions from the accumulated data and the knowledge in order to identify electronic threat patterns and create alerts. The architecture of the invention supports two main modes of operation: a. an automated, continuous mode for monitoring, recognition and detection of known eThreats; and b. an interactive, human-operated intelligent tool for dynamic exploration of the contents of a security storage service to identify new temporal patterns that characterize such threats, and to add them to the monitoring database. The architecture of the invention can analyze data collected from various sources, such as end-user devices, network element, network links etc., to identify potentially infected devices, files, sub-streams or network segments.