Abstract: In some examples, a system receives information traffic communicated over a network by or with a system under test (SUT), and analyzes the information traffic to identify a potential attack point in the SUT and a technology used by the SUT. The system determines a collection of penetration tests for testing a stack comprising a plurality of layers associated with the SUT based on the identified potential attack point and the identified technology, and further based on a dynamic knowledge base that includes information relating to vulnerabilities and threats.

Abstract: Examples described herein relate to a management node and a method for managing migration of workload resources. The management node may assign a capability tag to each of a plurality of member nodes hosting workload resources. Further, the management node may determine a resource requirement classification of each workload resource of the workload resources based on analysis of runtime performance data of each workload resource. Furthermore, the management node may determine a temporal usage pattern classification of each workload resource. Moreover, the management node may determine a migration plan for a candidate workload resource of the workload resources based on the capability tag of each of the plurality of member nodes, the resource requirement classification and the temporal usage pattern classification of each workload resource.

Abstract: In some examples, a system receives information traffic communicated over a network by or with a system under test (SUT), and analyzes the information traffic to identify a potential attack point in the SUT and a technology used by the SUT. The system determines a collection of penetration tests for testing a stack comprising a plurality of layers associated with the SUT based on the identified potential attack point and the identified technology, and further based on a dynamic knowledge base that includes information relating to vulnerabilities and threats.

Abstract: A system for detecting a network intrusion includes a first neural network for determining a first plurality of weight values corresponding to a plurality of vectors of an input data, a second neural network for updating the first plurality of weight values received from the first neural network to a second plurality of weight values based on the plurality of vectors of the input data, a third neural network for updating the second plurality of weight values received from the second neural network to a third plurality of weight values based on the plurality of vectors of the input data, and a classification module for classifying the plurality of vectors under at least one of a plurality of intrusions based on the third plurality of weight values received from the third neural network.