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    ANOMALOUS ENTITY DETERMINATIONS
    12.
    发明申请
    ANOMALOUS ENTITY DETERMINATIONS 审中-公开

    公开(公告)号：US20180337935A1

    公开(公告)日：2018-11-22

    申请号：US15596042

    申请日：2017-05-16

    Applicant: EntIT Software LLC

    Inventor： Manish Marwah ,  Alexander Ulanov ,  Carlos Zubieta ,  Luis Mateos ,  Pratyusa K. Manadhata

    IPC: H04L29/06

    Abstract: In some examples, a system generates a graphical representation of entities associated with a computing environment, and derives features for the entities represented by the graphical representation, the features comprising neighborhood features and link-based features, a neighborhood feature for a first entity of the entities derived based on entities that are neighbors of the first entity in the graphical representation, and a link-based feature for the first entity derived based on relationships of other entities in the graphical representation with the first entity. The system determines, using a plurality of anomaly detectors based on respective features of the derived features, whether the first entity is exhibiting anomalous behavior.

    DETERMINING POTENTIALLY MALWARE GENERATED DOMAIN NAMES
    14.
    发明申请
    DETERMINING POTENTIALLY MALWARE GENERATED DOMAIN NAMES 审中-公开

    公开(公告)号：US20190334931A1

    公开(公告)日：2019-10-31

    申请号：US15963336

    申请日：2018-04-26

    Applicant: ENTIT SOFTWARE LLC

    Inventor： Martin Arlitt ,  Pratyusa K. Manadhata

    IPC: H04L29/06 ,  H04L29/12

    Abstract: In some examples, a Domain Name System (DNS) server is to receive, over a network, a DNS query containing a domain name, the DNS query sent by a device. The DNS server is to determine whether the domain name is potentially generated by malware. In response to determining that the domain name is potentially generated by malware, the DNS server is to generate a DNS response containing information indicating that the domain name is potentially generated by malware, and send the DNS response to the network.

    UNAUTHORIZED AUTHENTICATION EVENT DETECTION
    15.
    发明申请
    UNAUTHORIZED AUTHENTICATION EVENT DETECTION 审中-公开

    公开(公告)号：US20190327253A1

    公开(公告)日：2019-10-24

    申请号：US15959461

    申请日：2018-04-23

    Applicant: ENTIT SOFTWARE LLC

    Inventor： Pratyusa K. Manadhata ,  Mijung Kim

    IPC: H04L29/06 ,  G06K9/62

    Abstract: In some examples, a system identifies, for a given authentication event between a plurality of devices in a network, a context comprising a set of authentication events that are temporally related to the given authentication event. The set of authentication events occur at the devices. A classifier is applied on a collection of features associated with the set of authentication events, the collection of features comprising a number of machines or a number of users associated with the set of authentication events. The system determines, based on an output of the classifier, whether the given authentication event is an unauthorized authentication event.

    TRAINING MODELS BASED ON BALANCED TRAINING DATA SETS
    16.
    发明申请
    TRAINING MODELS BASED ON BALANCED TRAINING DATA SETS 审中-公开

    公开(公告)号：US20190064752A1

    公开(公告)日：2019-02-28

    申请号：US15689047

    申请日：2017-08-29

    Applicant: EntIT Software LLC

    Inventor： Manish Marwah ,  Mijung Kim ,  Pratyusa K. Manadhata

    IPC: G05B13/02 ,  G06F15/18

    Abstract: In some examples, a system balances a number of positive data points and a number of negative data points, to produce a balanced training data set, where the positive data points comprise features associated with authentication events that are positive with respect to an unauthorized classification, and the negative data points comprise features associated with authentication events that are negative with respect to the unauthorized classification. The system trains a plurality of models using the balanced training data set, wherein the plurality of models are trained according to respective different machine learning techniques. The system selects a model from the trained plurality of models based on relative performance of the plurality of models.

    Determining whether domain is benign or malicious
    17.
    发明授权
    Determining whether domain is benign or malicious 有权

    公开(公告)号：US11245720B2

    公开(公告)日：2022-02-08

    申请号：US16433151

    申请日：2019-06-06

    Applicant: ENTIT Software LLC

    Inventor： Pratyusa K. Manadhata ,  Martin Arlitt

    IPC: H04L29/06

    Abstract: For each of a number of naming deviation types, the number of deviations within a domain name of a domain is determined. Each naming deviation type is a different type of deviation from domain name naming rules. For each naming deviation type for which the number of deviations is non-zero, first benign and malicious probabilities that benign and malicious domains, respectively, have the naming deviation type are estimated. Second benign and malicious probabilities that any given domain is respectively benign and malicious are estimated. Probabilities that the domain is benign and malicious are estimated based on the number of deviations for each naming deviation type and based on the estimated first and second benign and malicious probabilities. Whether the domain is benign or malicious is determined based on the estimated probabilities that the domain is benign and malicious.

    EXTRACTING FEATURES FOR AUTHENTICATION EVENTS
    20.
    发明申请
    EXTRACTING FEATURES FOR AUTHENTICATION EVENTS 审中-公开

    公开(公告)号：US20190065762A1

    公开(公告)日：2019-02-28

    申请号：US15689045

    申请日：2017-08-29

    Applicant: EntIT Software LLC

    Inventor： Mijung Kim ,  Pratyusa K. Manadhata ,  Manish Marwah

    IPC: G06F21/60 ,  H04L29/06 ,  G06F17/30

    Abstract: In some examples, for a given authentication event between a plurality of devices in a network, a system identifies a set of events, at the devices, that are temporally related to the given authentication event. The system extracts features from the set of events by aggregating event data of the set of events. The system provides the extracted features to a classifier that detects unauthorized authentication events.

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