SECURING SOFTWARE INSTALLATION THROUGH DEEP GRAPH LEARNING

    公开(公告)号:WO2021030133A1

    公开(公告)日:2021-02-18

    申请号:PCT/US2020/045150

    申请日:2020-08-06

    Abstract: A computer-implemented method for securing software installation through deep graph learning includes extracting (810) a new software installation graph (SIG) corresponding to a new software installation based on installation data associated with the new software installation, using (820) at least two node embedding models to generate a first vector representation by embedding the nodes of the new SIG and inferring any embeddings for out-of-vocabulary (OOV) words corresponding to unseen pathnames, utilizing (830) a deep graph autoencoder to reconstruct nodes of the new SIG from latent vector representations encoded by the graph LSTM, wherein reconstruction losses resulting from a difference of a second vector representation generated by the deep graph autoencoder and the first vector representation represent anomaly scores for each node, and performing (840) anomaly detection by comparing an overall anomaly score of the anomaly scores to a threshold of normal software installation.

    INTER-APPLICATION DEPENDENCY ANALYSIS FOR IMPROVING COMPUTER SYSTEM THREAT DETECTION

    公开(公告)号:WO2019032180A1

    公开(公告)日:2019-02-14

    申请号:PCT/US2018/037183

    申请日:2018-06-13

    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.

    METHOD FOR AUTOMATED CODE REVIEWER RECOMMENDATION

    公开(公告)号:WO2021055239A1

    公开(公告)日:2021-03-25

    申请号:PCT/US2020/050302

    申请日:2020-09-11

    Abstract: A method for automatically recommending a reviewer for submitted codes is presented. The method includes employing (801), in a learning phase, an artificial intelligence agent for learning an underlying and contextual structure of code regions, mapping (803) the code regions into a distributed representation to define code region representations, employing (805), in a recommendation phase, the artificial intelligence agent to produce a ranked list of recommended reviewers for any given submitted code review request, and outputting (807) the ranked list of recommended reviewers to a visualization device.

    TIMELY CAUSALITY ANALYSIS IN HOMEGENEOUS ENTERPRISE HOSTS

    公开(公告)号:WO2018213061A2

    公开(公告)日:2018-11-22

    申请号:PCT/US2018/031559

    申请日:2018-05-08

    CPC classification number: G06F21/554 G06F2221/034

    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 (720F), 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 (720G), 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 (720G), 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 (730), by the processor, an action to improve a functioning of the hosts responsive to the dependency graph or information derived therefrom.

    TIMELY CAUSALITY ANALYSIS IN HOMEGENEOUS ENTERPRISE HOSTS

    公开(公告)号:WO2018213061A3

    公开(公告)日:2018-11-22

    申请号:PCT/US2018/031559

    申请日:2018-05-08

    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 (720F), 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 (720G), 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 (720G), 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 (730), by the processor, an action to improve a functioning of the hosts responsive to the dependency graph or information derived therefrom.

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