Emc IP Holding Company LLC

Other

Deutschetelekom Ag

Ehud Gudes

Patricia Gomes Soares Florissi

Sudhir Vijendra

Ido Singer

Amnon Meisels

Yaron Gonen

Benny Lutati

Yuval Elovici

Michal Ziv Ukelson

Ran Dach

Arnon Benshahar

Erez Shmueli

Boris Rozenberg

Dan Hendler

Nurit Gal-Oz

Nurit Galoz

An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective sample sources, with each of the biological samples containing genomic material from a plurality of distinct microorganisms within an environment of a corresponding one of the sample sources, and to perform distributed data analytics to characterize an actual or potential outbreak of at least one of a disease, an infection and a contamination that involves genomic material from multiple ones of the distinct microorganisms in one or more of the sample sources. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Distributed data analytics

A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Distributed catalog service for multi-cluster data processing platform

A method comprises configuring a first processing node for communication with one or more additional processing nodes and with one or more of a plurality of geographically-distributed metagenomics sequencing centers via one or more networks, processing metagenomics sequencing results obtained from one or more of the metagenomics sequencing centers in the first processing node, and providing surveillance functionality relating to at least one designated biological issue on behalf of one or more requesting clients based at least in part on the processing of metagenomics sequencing results performed by the first processing node and related processing performed by one or more of the additional processing nodes. Each of the metagenomics sequencing centers is configured to perform metagenomics sequencing on biological samples from respective sample sources in a corresponding data zone.

Distributed data processing platform for metagenomic monitoring and characterization

An apparatus comprises a multi-cluster distributed data processing platform. Each of the clusters of the platform is configured to perform processing operations utilizing local data resources accessible within a corresponding data zone. A first application is initiated in one of the clusters, and data resources to be utilized by the application are determined. For one or more of the data resources identified as local data resources for the associated cluster, processing operations are performed utilizing those local data resources in that cluster in accordance with the first application. For one or more of the data resources identified as remote data resources for the associated cluster, one or more additional applications are initiated in one or more additional ones of the clusters. This repeats recursively for each additional application until all processing required by the first application is complete. Processing results from the clusters are aggregated and provided to a client.

Multi-cluster distributed data processing platform

The invention relates to a method for detecting malicious executables, which comprises: in an offline training phase, finding a collection of system call sequences that are characteristic only to malicious files, when such malicious files are executed, and storing said sequences in a database; and, in runtime, for each running executable, continuously monitoring its issued run-time system calls and comparing with the stored sequences of system calls within the database to determine whether there exists a match between a portion of the sequence of the run-time system calls and one or more of the database sequences, and when such a match is found, declaring said executable as malicious.

System and method for detecting new malicious executables, based on discovering and monitoring characteristic system call sequences

The invention relates to a distributed system for detecting eThreats that propagate in a network, which comprises: (a) graphs database storing at least one propagation graph, each graph describing the typical propagation over time of one eThreat class or a legitimate executable class within the network; (b) plurality of agents that are distributed in corresponding plurality of hosts within the network, each of said agents continuously monitoring the corresponding host and reporting to a Central Decision Maker (CDM) the identity of any new suspected executable, and the time in which said suspected executable has been first detected by said agent; (c) a CDM for: (c.1) receiving all said reports from said plurality of agents; (c.2) creating from said reports for each suspected executable a corresponding propagation graph which reflects the propagation characteristics over time of said suspected executable within the network, and (c.3) comparing each of said created graphs with said stored at least one propagation graph; (c.4) upon finding a similarity above a predefined threshold between a created graph and one of the stored graphs, concluding respectively that said executable belongs to the class as defined by said stored graph; and (c.5) conveying said conclusion to said agents, for optionally taking an appropriate action.

Distributed system and method for the detection of eThreats

Growing community of inventors

Beer-Sheva, Israel

Ehud Gudes