Abstract: A web browser extension is operable to identify content in a web page that is potentially misinformation, and to compare the at least one content element against a database of verified information. The comparison process determines whether a verified information element in the database corresponds to the at least one content element, and if a verified information element in the database is found to correspond to the at least one content element, the verified information element in the database is displayed in association with the at least one content element via the web browser.

Abstract: Systems and methods enable a notification based on determining a particular electronic message is associated with a particular cluster of electronic messages. A plurality of electronic messages from a first plurality of accounts directed to a second plurality of accounts over a network are received. The plurality of electronic messages are compared to determine a plurality of clusters of electronic messages. A particular electronic message is received from a first particular account directed to a second particular account. The particular electronic message is compared to the plurality of clusters of electronic messages to determine that the particular electronic message is associated with a particular cluster of the plurality of clusters of electronic messages. A notification is provided based on the determining that the particular electronic message is associated with the particular cluster of the plurality of clusters of electronic messages.

Abstract: A malware classification system includes a first machine-learning model trained based on malware from a first plurality of prior time periods to predict malware in a first subsequent time period subsequent to the first plurality of prior time periods, and a second machine-learning model is trained based on malware from a second plurality of prior time periods offset by at least some time from the plurality of time periods used to train the first machine-learning model to predict malware in a second subsequent time period subsequent to the second plurality of prior time periods. The trained first and second machine-learning models are used to predict malware in a future time period, and a classifier is trained using the malware from a plurality of the prior time periods and predicted malware from a future time period to train the classifier to identify and/or classify malware.