Abstract: An example operation may include one or more of dividing a data file into a plurality of data chunks, generating a randomness value for each data chunk based on one or more predefined randomness tests, and accumulating generated randomness values of the plurality of data chunks to generate an accumulated randomness value, detecting whether the data file is one or more of encrypted and compressed based on the accumulated randomness value and a predetermined threshold value, and storing information about the detection via a storage.

Abstract: Embodiments provide a computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor to implement the method of identifying an exploit kit, the method comprising: receiving, by the processor, a web page; extracting, by the processor, a plurality of features of the web page; and determining, by the processor, whether the web page is associated with an exploit kit, through an ensemble classifier model trained using the extracted features.

Abstract: Mechanisms are provided to perform security incident disposition operations. A security incident is received that includes a security incident data structure comprising metadata describing properties of the security incident, and a corresponding security knowledge graph which includes nodes representing elements associated with the security incident and edges representing relationships between the nodes. The security incident data structure and security knowledge graph are processed to extract a set of security incident features corresponding to the security incident and input the extracted set of security incident features into a trained security incident machine learning model. The model generates a disposition classification output based on results of processing the extracted set of security incident features. The disposition classification output is output to the source of the security incident data structure.

Abstract: An example operation may include one or more of dividing a data file into a plurality of data chunks, generating a randomness value for each data chunk based on one or more predefined randomness tests, and accumulating generated randomness values of the plurality of data chunks to generate an accumulated randomness value, detecting whether the data file is one or more of encrypted and compressed based on the accumulated randomness value and a predetermined threshold value, and storing information about the detection via a storage.

Abstract: An example operation may include one or more of dividing a data file into a plurality of data chunks, generating a randomness value for each data chunk based on one or more predefined randomness tests, and accumulating generated randomness values of the plurality of data chunks to generate an accumulated randomness value, detecting whether the data file is one or more of encrypted and compressed based on the accumulated randomness value and a predetermined threshold value, and storing information about the detection via a storage.

Abstract: Mechanisms are provided to perform security incident disposition operations. A security incident is received that includes a security incident data structure comprising metadata describing properties of the security incident, and a corresponding security knowledge graph which includes nodes representing elements associated with the security incident and edges representing relationships between the nodes. The security incident data structure and security knowledge graph are processed to extract a set of security incident features corresponding to the security incident and input the extracted set of security incident features into a trained security incident machine learning model. The model generates a disposition classification output based on results of processing the extracted set of security incident features. The disposition classification output is output to the source of the security incident data structure.

Abstract: An example operation may include one or more of dividing a data file into a plurality of data chunks, generating a randomness value for each data chunk based on one or more predefined randomness tests, and accumulating generated randomness values of the plurality of data chunks to generate an accumulated randomness value, detecting whether the data file is one or more of encrypted and compressed based on the accumulated randomness value and a predetermined threshold value, and storing information about the detection via a storage.

Abstract: Embodiments provide a computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor to implement the method of identifying an exploit kit, the method comprising: receiving, by the processor, a web page; extracting, by the processor, a plurality of features of the web page; and determining, by the processor, whether the web page is associated with an exploit kit, through an ensemble classifier model trained using the extracted features.