ABNORMAL BEHAVIOR DETECTION MODEL BUILDING APPARATUS AND ABNORMAL BEHAVIOR DETECTION MODEL BUILDING METHOD THEREOF

    公开(公告)号:US20190180030A1

    公开(公告)日:2019-06-13

    申请号:US15839839

    申请日:2017-12-12

    Abstract: An abnormal behavior detection model building apparatus and an abnormal behavior detection model building method thereof are provided. The abnormal behavior detection model building apparatus analyzes the parts of speech of a plurality of program operation sequences in a plurality of program operation sequence data associated with abnormal behaviors to generate a plurality of word vectors and cluster the word vectors. Based on the result of the clustering, the abnormal behavior detection model building apparatus obtains a feature vector of each of the program operation sequence data, and perform a supervised learning for a classification algorithm by using the feature vectors so as to build an abnormal behavior detection model.

    NETWORK ATTACK PATTERN DETERMINATION APPARATUS, DETERMINATION METHOD, AND NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM THEREOF

    公开(公告)号:US20180159868A1

    公开(公告)日:2018-06-07

    申请号:US15372294

    申请日:2016-12-07

    Abstract: A network attack pattern determination apparatus, method, and non-transitory computer readable storage medium thereof are provided. The apparatus is stored with several attack patterns and access records. Each access record includes a network address, time stamp, and access content. Each attack pattern corresponds to at least one attack access relation. Each attack access relation is defined by a network address and access content. The apparatus retrieves several attack records according to at least one attack address. The network address of each attack record is one of the attack address(s). The apparatus divides the attack records into several groups according to the time stamps and performs the following operations for each group: (a) creating at least one access relation for each attack address included in the group and (b) determining that the group corresponds to one of the attack patterns according to the at least one access relation of the group.

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