Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that improve detection of malware based on ecosystem specific data. An example apparatus includes a feedback weight controller to apply, with a machine learning model, a weight to feedback associated with a sample, the feedback obtained from at least a customer ecosystem and including endpoint feedback, human feedback, infrastructure feedback, and global feedback; and a sample conviction controller to, in response to a score based on the weighted feedback satisfying a threshold for a classification, indicate to a user, with the machine learning model, that the classification for the sample is malicious.

Abstract: Example apparatus disclosed herein generate blocks of a blockchain, the blockchain to store a neural network that has input nodes, hidden nodes and output nodes, with respective ones of the blocks of the blockchain including respective code and respective data to represent corresponding ones of the output nodes of the neural network, a first one of the blocks including first code and first data to implement operations to be performed by a first one of the output nodes, the hidden nodes and the input nodes on input data applied to the neural network to determine an output of the first one of the output nodes. Disclosed example apparatus also train the neural network to determine at least portions of the respective data to include in the respective ones of the blocks of the blockchain, and forward the blockchain to a server that is to distribute the neural network to client(s).

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that improve detection of malware based on ecosystem specific data. An example apparatus includes a feedback weight controller to apply, with a machine learning model, a weight to feedback associated with a sample, the feedback obtained from at least a customer ecosystem and including endpoint feedback, human feedback, infrastructure feedback, and global feedback; and a sample conviction controller to, in response to a score based on the weighted feedback satisfying a threshold for a classification, indicate to a user, with the machine learning model, that the classification for the sample is malicious.

Abstract: Example apparatus disclosed herein generate blocks of a blockchain, the blockchain to store a neural network that has input nodes, hidden nodes and output nodes, with respective ones of the blocks of the blockchain including respective code and respective data to represent corresponding ones of the output nodes of the neural network, a first one of the blocks including first code and first data to implement operations to be performed by a first one of the output nodes, the hidden nodes and the input nodes on input data applied to the neural network to determine an output of the first one of the output nodes. Disclosed example apparatus also train the neural network to determine at least portions of the respective data to include in the respective ones of the blocks of the blockchain, and forward the blockchain to a server that is to distribute the neural network to client(s).

Abstract: Methods, apparatus, systems and articles of manufacture to detect steganographically hidden content in a media file are disclosed. An example system includes a media classifier to determine type of a media file, and a detector to apply a detection technique to the media file. The detector selects the detection technique from a plurality of steganographically-based detection techniques based on the media file type. The system also includes a remediator to apply a remediation technique to the media file based on whether the detector detects steganographically hidden content in the media file.

Abstract: Methods, apparatus, systems and articles of manufacture to detect steganographically hidden content in a media file are disclosed. An example system includes a media classifier to determine type of a media file, and a detector to apply a detection technique to the media file. The detector selects the detection technique from a plurality of steganographically-based detection techniques based on the media file type. The system also includes a remediator to apply a remediation technique to the media file based on whether the detector detects steganographically hidden content in the media file.