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公开(公告)号：US20220413847A1
公开(公告)日：2022-12-29
申请号：US17358166
申请日：2021-06-25
摘要： A content generation method includes receiving a control document comprising one or more control clauses, identifying actionable content for the one or more control clauses, generating a programming language template for the one or more control clauses, identifying a closest existing control clause from a database for each of the one or more control clause, identifying a programming language implementation of the closest existing control clause, identifying similarities and differences between the programming language implementation and the generated programming language template, and annotating the programming language implementation for the closest existing control clause based on the identified similarities and differences. The method may additionally include determining whether a closest existing control clause exists, providing the generated programming language template to a user responsive to determining that a closest existing control clause does not exist, and receiving feedback from the user regarding the generated programming language template.
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公开(公告)号：US11488064B2
公开(公告)日：2022-11-01
申请号：US16834463
申请日：2020-03-30
发明人： Muhammed Fatih Bulut , Jinho Hwang , Ali Kanso , Shripad Nadgowda
摘要： Embodiments relate to a computer system, computer program product, and computer-implemented method to train a machine learning (ML) model using artificial intelligence to learn an association between (regulatory) compliance requirements and features of micro-service training datasets. The trained ML model is leveraged to determine the compliance requirements of a micro-service requiring classification. In an exemplary embodiment, once the micro-service has been classified with respect to applicable compliance requirements, the classified micro-service may be used as an additional micro-service training dataset to further train the ML model and thereby improve its performance.
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公开(公告)号：US20220131888A1
公开(公告)日：2022-04-28
申请号：US17078603
申请日：2020-10-23
发明人： Ali Kanso , Muhammed Fatih Bulut , Jinho Hwang , Shripad Nadgowda
IPC分类号： H04L29/06
摘要： According to an embodiment, a computer-implemented method can comprise: inspecting, using a processor, a set of container images respectively associated with pods; identifying, using the processor, a first subset of the pods that contain a vulnerability; classifying, using the processor, the first subset of the pods as primary-infected pods; generating, using the processor, a first list of namespaces in which the primary-infected pods are deployed within a network; checking, using the processor, network policies in connection with the first list of namespaces to determine secondary-suspect pods that have ability to communicate with the primary-infected pods; generating, using the processor, a list of secondary-suspect namespaces in which the secondary-suspect pods are deployed within the network; identifying, using the processor, one or more secondary-suspect pods that communicated with one or more primary-infected pods; and generating, using the processor, a list of secondary-infected pods.
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公开(公告)号：US11146586B2
公开(公告)日：2021-10-12
申请号：US16734322
申请日：2020-01-04
发明人： Muhammed Fatih Bulut , Lisa Chavez , Jinho Hwang , Anup Kalia , Virginia Mayo Policarpio , Sai Zeng
摘要： A method and system of identifying a computing device vulnerability is provided. Social media communication is monitored. Social media threads that are related to a vulnerability, based on the monitored social media communication, are identified, filtered, and categorized into one or more predetermined categories of computing device vulnerabilities. Upon determining that a number of social media posts related to the vulnerability is above a first predetermined threshold, one or more dependable social media threads in a same one or more categories as the vulnerability are searched. One or more possible root causes of the vulnerability are determined from the searched dependable social media threads. A validity score for each of the one or more possible root causes is assigned. A possible root cause from that has a highest validity score that is above a second predetermined threshold is selected to be the root cause of the vulnerability.
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公开(公告)号：US10778713B2
公开(公告)日：2020-09-15
申请号：US15904529
申请日：2018-02-26
发明人： Sai Zeng , Vugranam C. Sreedhar , Karin Murthy , Jinho Hwang , Milton H. Hernandez , Lisa M. Chavez , Muhammed Fatih Bulut , Virginia Mayo , Xinli Wang , Cindy Mullen
摘要： A system includes a memory that stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An assessment component assesses a computer network, and classifies the computer network relative to M network classifications stored in a repository, wherein M is an integer greater than one. A risk component determines risk of vulnerability subject to change impact regarding protection against a computer virus or cyber-attack based on historical information regarding vulnerability exposure and vulnerability remediation changes relative to the classification of the computer network. A recommendation component that generates recommendations and best action to mitigate risk and impact, and remediate the vulnerabilities based on the risk assessment and business priorities.
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公开(公告)号：US10649758B2
公开(公告)日：2020-05-12
申请号：US15800612
申请日：2017-11-01
发明人： Muhammed Fatih Bulut , Lisa M. Chavez , Jinho Hwang , Virginia Mayo , Vugranam C. Sreedhar , Sai Zeng
摘要： Techniques that facilitate group patching recommendation and/or remediation with risk assessment are provided. In one example, a system includes a vertical stack component, a horizontal stack component and a risk classification component. The vertical stack component identifies a first patch profile from a software system associated with a computer system environment. The horizontal stack component identifies a second patch profile from a hardware system associated with network nodes of the computer system environment. The system learns over time to identify repetitive patterns using machine learning techniques. Then, the risk classification component performs a machine learning process to determine a risk classification for the computer system environment based on the first patch profile and the second patch profile.
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公开(公告)号：US10587643B2
公开(公告)日：2020-03-10
申请号：US15825089
申请日：2017-11-28
发明人： Muhammed Fatih Bulut , Lisa Chavez , Jinho Hwang , Anup Kalia , Virginia Mayo Policarpio , Sai Zeng
摘要： A method and system of identifying a computing device vulnerability is provided. Social media communication is monitored. Social media threads that are related to a vulnerability, based on the monitored social media communication, are identified, filtered, and categorized into one or more predetermined categories of computing device vulnerabilities. Upon determining that a number of social media posts related to the vulnerability is above a first predetermined threshold, one or more dependable social media threads in a same one or more categories as the vulnerability are searched. One or more possible root causes of the vulnerability are determined from the searched dependable social media threads. A validity score for each of the one or more possible root causes is assigned. A possible root cause from that has a highest validity score that is above a second predetermined threshold is selected to be the root cause of the vulnerability.
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公开(公告)号：US10540496B2
公开(公告)日：2020-01-21
申请号：US15721566
申请日：2017-09-29
摘要： Techniques for dynamic server groups that can be patched together using stream clustering algorithms, and learning components in order to reuse the repeatable patterns using machine learning are provided herein. In one example, in response to a first risk associated with a first server device, a risk assessment component patches a server group to mitigate a vulnerability of the first server device and a second server device, wherein the server group is comprised of the first server device and the second server device. Additionally, a monitoring component monitors data associated with a second risk to the server group to mitigate the second risk to the server group.
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公开(公告)号：US20190166151A1
公开(公告)日：2019-05-30
申请号：US15825089
申请日：2017-11-28
发明人： Muhammed Fatih Bulut , Lisa M. Chavez , Jinho Hwang , Anup Kalia , Virginia Mayo , Sai Zeng
摘要： A method and system of identifying a computing device vulnerability is provided. Social media communication is monitored. Social media threads that are related to a vulnerability, based on the monitored social media communication, are identified, filtered, and categorized into one or more predetermined categories of computing device vulnerabilities. Upon determining that a number of social media posts related to the vulnerability is above a first predetermined threshold, one or more dependable social media threads in a same one or more categories as the vulnerability are searched. One or more possible root causes of the vulnerability are determined from the searched dependable social media threads. A validity score for each of the one or more possible root causes is assigned. A possible root cause from that has a highest validity score that is above a second predetermined threshold is selected to be the root cause of the vulnerability.
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50.发明授权公开(公告)号：US12124924B2
公开(公告)日：2024-10-22
申请号：US16950228
申请日：2020-11-17
发明人： Ali Kanso , Jinho Hwang , Muhammed Fatih Bulut , Shripad Nadgowda , Chen Lin
摘要： Systems and methods are provided that integrate a machine-learning model, and more specifically, utilizing a platform as a service (PaaS) cloud to predict probability of success for an operator in an environment. An embodiment comprises a system having: a processor that executes computer executable components stored in memory, trained machine-learning model that predicts probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud, and a deployment component that receives a first operator and a first namespace and employs the trained machine-learning model to predict success of deployment of the first operator in a first environment.
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