Confidential machine learning with program compartmentalization

    公开(公告)号:US11423142B2

    公开(公告)日:2022-08-23

    申请号:US16693710

    申请日:2019-11-25

    Abstract: A method for implementing confidential machine learning with program compartmentalization includes implementing a development stage to design an ML program, including annotating source code of the ML program to generate an ML program annotation, performing program analysis based on the development stage, including compiling the source code of the ML program based on the ML program annotation, inserting binary code based on the program analysis, including inserting run-time code into a confidential part of the ML program and a non-confidential part of the ML program, and generating an ML model by executing the ML program with the inserted binary code to protect the confidentiality of the ML model and the ML program from attack.

    Inter-application dependency analysis for improving computer system threat detection

    公开(公告)号:US11030308B2

    公开(公告)日:2021-06-08

    申请号:US16006164

    申请日:2018-06-12

    Abstract: A method and system are provided for improving threat detection in a computer system by performing an inter-application dependency analysis on events of the computer system. The method includes receiving, by a processor operatively coupled to a memory, a Tracking Description Language (TDL) query including general constraints, a tracking declaration and an output specification, parsing, by the processor, the TDL query using a language parser, executing, by the processor, a tracking analysis based on the parsed TDL query, generating, by the processor, a tracking graph by cleaning a result of the tracking analysis, and outputting, by the processor and via an interface, query results based on the tracking graph.

    Template based data reduction for commercial data mining

    公开(公告)号:US11030157B2

    公开(公告)日:2021-06-08

    申请号:US15979514

    申请日:2018-05-15

    Abstract: Systems and methods for mining and compressing commercial data including a network of point of sale devices to log commercial activity data including independent commercial events and corresponding dependent features. A middleware system is in communication with the network of point of sale devices to continuously collect and compress a stream of the commercial activity data and concurrently store the compressed commercial activity data. Compressing the stream includes a file access table corresponding to the commercial activity data, producing compressible file access templates (CFATs) according to frequent patterns of commercial activity data using the file access table, and replacing dependent feature sequences with a matching compressible file access template. A database is in communication with the middleware system to store the compressed commercial data. A commercial pattern analysis system is in communication with the database to determine patterns in commercial activities across the network of point of sale devices.

    INTER-APPLICATION DEPENDENCY ANALYSIS FOR IMPROVING COMPUTER SYSTEM THREAT DETECTION

    公开(公告)号:US20190050561A1

    公开(公告)日:2019-02-14

    申请号:US16006164

    申请日:2018-06-12

    Abstract: A method and system are provided for improving threat detection in a computer system by performing an inter-application dependency analysis on events of the computer system. The method includes receiving, by a processor operatively coupled to a memory, a Tracking Description Language (TDL) query including general constraints, a tracking declaration and an output specification, parsing, by the processor, the TDL query using a language parser, executing, by the processor, a tracking analysis based on the parsed TDL query, generating, by the processor, a tracking graph by cleaning a result of the tracking analysis, and outputting, by the processor and via an interface, query results based on the tracking graph.

    TIMELY CAUSALITY ANALYSIS IN HOMEGENEOUS ENTERPRISE HOSTS

    公开(公告)号:US20180336349A1

    公开(公告)日:2018-11-22

    申请号:US15972911

    申请日:2018-05-07

    Abstract: A method and system are provided for causality analysis of Operating System-level (OS-level) events in heterogeneous enterprise hosts. The method includes storing, by the processor, the OS-level events in a priority queue in a prioritized order based on priority scores determined from event rareness scores and event fanout scores for the OS-level events. The method includes processing, by the processor, the OS-level events stored in the priority queue in the prioritized order to provide a set of potentially anomalous ones of the OS-level events within a set amount of time. The method includes generating, by the processor, a dependency graph showing causal dependencies of at least the set of potentially anomalous ones of the OS-level events, based on results of the causality dependency analysis. The method includes initiating, by the processor, an action to improve a functioning of the hosts responsive to the dependency graph or information derived therefrom.

    REAL-TIME THREAT ALERT FORENSIC ANALYSIS
    19.
    发明申请

    公开(公告)号:US20200250308A1

    公开(公告)日:2020-08-06

    申请号:US16781366

    申请日:2020-02-04

    Abstract: Methods and systems for security monitoring and response include assigning an anomaly score to each of a plurality of event paths that are stored in a first memory. Events that are cold, events that are older than a threshold, and events that are not part of a top-k anomalous path are identified. The identified events are evicted from the first memory to a second memory. A threat associated with events in the first memory is identified. A security action is performed responsive to the identified threat.

    AUTOMATED THREAT ALERT TRIAGE VIA DATA PROVENANCE

    公开(公告)号:US20200042700A1

    公开(公告)日:2020-02-06

    申请号:US16507353

    申请日:2019-07-10

    Abstract: A method for implementing automated threat alert triage via data provenance includes receiving a set of alerts and security provenance data, separating true alert events within the set of alert events corresponding to malicious activity from false alert events within the set of alert events corresponding to benign activity based on an alert anomaly score assigned to the at least one alert event, and automatically generating a set of triaged alert events based on the separation.

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