Abstract: Systems and method for automated determination of granular object storage service metrics are provided. In one example, metadata regarding a subset of objects stored in one or more shared containers of an object storage service is retrieved. One or more meta properties (e.g., a storage tier with which the object is associated, a size of the object, an object type, a file type, user-defined tags, and a full path of the object, including, for example, information indicative of the application and the tenant) may be obtained for each object of the subset of objects. The subset of objects satisfy a set of one or more criteria involving the metadata and/or the meta properties. A total storage cost and/or a total size of the subset of objects is determined by aggregating a storage cost and/or a size of each object of the subset of objects.

Abstract: Disclosed herein are systems and methods for detecting script code malware and generating signatures. A plurality of script code samples are received and transformed into a plurality of tokenized samples. The tokenized samples are based on syntactical elements of the plurality of script code samples. One or more clusters of samples are determined based on similarities in different ones of the plurality of tokenized samples, and known malicious code having a threshold similarity to a representative sample of the cluster of samples is identified. Based on the identifying, the cluster of samples is identified as malicious. Based at least on respective ones of the plurality of tokenized samples associated with the cluster of samples, a generalized code signature usable to identify the script code samples in the cluster of samples is generated.

Abstract: Disclosed herein are systems and methods for detecting script code malware and generating signatures. A plurality of script code samples are received and transformed into a plurality of tokenized samples. The tokenized samples are based on syntactical elements of the plurality of script code samples. One or more clusters of samples are determined based on similarities in different ones of the plurality of tokenized samples, and known malicious code having a threshold similarity to a representative sample of the cluster of samples is identified. Based on the identifying, the cluster of samples is identified as malicious. Based at least on respective ones of the plurality of tokenized samples associated with the cluster of samples, a generalized code signature usable to identify the script code samples in the cluster of samples is generated.