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    INTERCEPT FOR ENCRYPTED COMMUNICATIONS
    2.
    发明公开
    INTERCEPT FOR ENCRYPTED COMMUNICATIONS 审中-公开

    公开(公告)号：US20240048590A1

    公开(公告)日：2024-02-08

    申请号：US18071435

    申请日：2022-11-29

    申请人： CA, Inc.

    发明人： Roelof Nico DU TOIT ,  Gary TOMIC ,  Ronald Andrew FREDERICK

    IPC分类号： H04L9/40

    CPC分类号： H04L63/166 ,  H04L63/0442 ,  H04L63/30

    摘要： Aspects of the disclosure include replacing, by a DNS proxy in DNS responses, a cryptographic key associated with a client-facing server for an origin content server with another cryptographic key received from a TLS proxy. A device may encrypt an extension of a ClientHello message with the other cryptographic key, such that the encrypted ClientHello (ECH) extension can be decrypted by the TLS proxy. The TLS proxy can then allow or deny the connection using a TLS intercept policy and decrypted information in the ClientHello message, and if the TLS connection is allowed, re-encrypt the ECH with the cryptographic key in the DNS response for the client-facing server to decrypt for establishment of the TLS connection with the origin content server. To preserve selective intercept while using ECH, a TLS Intercept Policy may be used to decide whether the TLS proxy feeds an Application Layer Proxy.

    Machine learning adversarial campaign mitigation on a computing device
    5.
    发明授权
    Machine learning adversarial campaign mitigation on a computing device 有权

    公开(公告)号：US11551137B1

    公开(公告)日：2023-01-10

    申请号：US16399725

    申请日：2019-04-30

    申请人： CA, Inc.

    发明人： Javier Echauz ,  Andrew B. Gardner ,  John Keith Kenemer ,  Jasjeet Dhaliwal ,  Saurabh Shintre

    IPC分类号： G06F21/00 ,  G06N20/00 ,  G06F21/56 ,  G06K9/62

    摘要： Machine learning adversarial campaign mitigation on a computing device. The method may include deploying an original machine learning model in a model environment associated with a client device; deploying a classification monitor in the model environment to monitor classification decision outputs in the machine learning model; detecting, by the classification monitor, a campaign of adversarial classification decision outputs in the machine learning model; applying a transformation function to the machine learning model in the model environment to transform the adversarial classification decision outputs to thwart the campaign of adversarial classification decision outputs; determining a malicious attack on the client device based in part on detecting the campaign of adversarial classification decision outputs; and implementing a security action to protect the computing device against the malicious attack.

    Systems and methods for producing adjustments to malware-detecting services
    6.
    发明授权
    Systems and methods for producing adjustments to malware-detecting services 有权

    公开(公告)号：US11461462B1

    公开(公告)日：2022-10-04

    申请号：US16138939

    申请日：2018-09-21

    申请人： CA, Inc.

    发明人： Qichao Lan ,  Junda Zhu ,  Shaolong Shu ,  Tao Cheng ,  Rudy Senstad

    IPC分类号： G06F21/55 ,  G06F21/56 ,  G06N20/00 ,  G06N7/00 ,  G06K9/62

    摘要： The disclosed computer-implemented method for producing adjustments to malware-detecting services may include (1) receiving, from a plurality of malware-detecting services executing on a plurality of client computing devices, a respective plurality of probability scores with corresponding model identifiers for an analyzed file and a plurality of respective identifiers describing the malware-detecting services, (2) building a training dataset from at least a portion of the received plurality of probability scores with corresponding model identifiers, and (3) performing a security action including (A) training, with the training dataset, a malware-detecting linear regression ensemble machine learning model that is specific to an identifier in the plurality of identifiers and (B) sending the trained linear regression ensemble machine learning model to one of the plurality of malware-detecting services executing on one of the client computing devices. Various other methods, systems, and computer-readable media are also disclosed.

    Systems and methods for protecting a cloud computing device from malware
    7.
    发明授权
    Systems and methods for protecting a cloud computing device from malware 有权

    公开(公告)号：US11411968B1

    公开(公告)日：2022-08-09

    申请号：US16574755

    申请日：2019-09-18

    申请人： CA, INC.

    发明人： Ashok Banerjee ,  Susan Hassall

    IPC分类号： H04L29/06 ,  H04L9/40 ,  G06F21/62

    摘要： The disclosed computer-implemented method for protecting a cloud computing device from malware may include (i) intercepting, at a computing device, a malicious attempt by the malware to (A) access sensitive information in an encrypted file stored on the computing device and (B) send the sensitive information to the cloud computing device and (ii) performing, responsive to the attempt to access the encrypted file, a security action. Various other methods, systems, and computer-readable media are also disclosed.

    Systems and methods for malware detection using localized machine learning
    8.
    发明授权
    Systems and methods for malware detection using localized machine learning 有权

    公开(公告)号：US11386208B1

    公开(公告)日：2022-07-12

    申请号：US16414341

    申请日：2019-05-16

    申请人： CA, Inc.

    发明人： Qichao Lan ,  Tao Cheng

    IPC分类号： G06F21/56 ,  G06K9/62 ,  G06N20/00

    摘要： The disclosed computer-implemented method for malware detection using localized machine learning may include (i) generating a global score for a file using a global machine learning model, (ii) generating a localized score for the file using a localized machine learning model, (iii) determining that the file is malware using the global score, the localized score, and the local conviction threshold, and (iv) in response to determining that the file is malware, performing a security action to protect the computing device against malware. Various other methods, systems, and computer-readable media are also disclosed.

    Pre-filtering detection of an injected script on a webpage accessed by a computing device
    9.
    发明授权
    Pre-filtering detection of an injected script on a webpage accessed by a computing device 有权

    公开(公告)号：US11303670B1

    公开(公告)日：2022-04-12

    申请号：US16435179

    申请日：2019-06-07

    申请人： CA, Inc.

    发明人： Candid Alex Wueest

    IPC分类号： H04L29/06 ,  H04L67/56 ,  G06F21/53 ,  G06N20/00 ,  H04L67/02

    摘要： Pre-filtering detection of an injected script on a webpage accessed by a computing device. The method may include receiving an indication of access to the webpage at a web browser of the computing device; identifying a web form associated with the webpage; determining that the webpage has been previously visited by the computing device; recording at least one current domain associated with at least one current object request made by the web form; determining a difference of a count of the at least one current domain associated with the at least one current object request and a count of at least one historical domain associated with at least one historical object request previously made by the webpage; identifying the webpage as suspicious based on determining that the difference is greater than zero and less than a domain threshold; and initiating a security action on the webpage based on the identifying.

    Identifying and protecting against an attack against an anomaly detector machine learning classifier
    10.
    发明授权
    Identifying and protecting against an attack against an anomaly detector machine learning classifier 有权

    公开(公告)号：US11297083B1

    公开(公告)日：2022-04-05

    申请号：US16541442

    申请日：2019-08-15

    申请人： CA Inc.

    发明人： Aditya Kuppa ,  Slawomir Grzonkowski

    IPC分类号： H04L29/06 ,  G06N20/00 ,  G06K9/62

    摘要： Identifying and protecting against an attack against an anomaly detector machine learning classifier (ADMLC). In some embodiments, a method may include identifying training data points in a manifold space for an ADMLC, dividing the manifold space into multiple subspaces, merging each of the training data points into one of the multiple subspaces, training a subclassifier for each of the multiple subspaces to determine a decision boundary for each of the multiple subspaces between normal training data points and anomalous training data points, receiving an input data point into the ADMLC, determining whether the input data point is an attack on the ADMLC due to a threshold number of the subclassifiers classifying the input data point as an anomalous input data point, and, in response to identifying the attack against the ADMLC, protecting against the attack.