Abstract: The disclosed computer-implemented method for preventing decentralized malware attacks may include (i) receiving, by a computing device, node data from a group of nodes over a network, (ii) training a machine learning model by shuffling the node data to generate a set of outputs utilized for predicting malicious data, (iii) calculating a statistical deviation for each output in the set of outputs from an aggregated output for the set of outputs, and (iv) identifying, based on the statistical deviation, an anomalous output in the set of outputs that is associated with one or more of the malicious nodes, the one or more malicious nodes hosting the malicious data. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.

Abstract: Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.