Micro Focus Lcc

Stephan F Jou

Maria Pospelova

Michael F Angelo

Martin Arlitt

Manish Marwah

Douglas M Grover

Venkatesh HariRama Subbu

Asad Narayanan

A system includes a processor and a memory coupled with and readable by the processor and storing therein a set of instructions. When executed by the processor, the processor is caused to receive application events associated with an application and create data records based on the application events. The processor is further caused to compute an interestingness value for each of the data records based on a goal of the application, assign the computed interestingness value to each of the data records and store each of the data records with the assigned interestingness value.

Systems and methods for entity interestingness-based retention and removal

A request to load an application into memory for execution is received. The application is stored in one or more blocks in a blockchain. The application is validated by running a blockchain hash. In response to validating the application by running the blockchain hash, the application is loaded from the blockchain into a memory. The application is then executed in the memory. This provides a secure method of loading an application into memory.

Secure execution environment for applications using blockchain

First-order anomaly scores are received from related anomaly detectors. Each first-order anomaly score indicates a likelihood of an anomaly at a target system. A relatedness measure of the related anomaly detectors is determined, based on the first-order anomaly scores that have been received. A higher-order anomaly score is determined based on the relatedness measure that has been determined. The higher-order anomaly score indicates a likelihood of an anomaly at the target system. An anomaly at the target system is detected based on the higher-order anomaly score.

Anomaly detection based on higher-order anomaly score

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Stephan F Jou