Abstract: Techniques for adaptive validation and remediation are described. In some embodiments, the method includes determining, for a plurality of media service accounts, corresponding fraud suspicion values based on a model. The method also includes identifying a plurality of suspected accounts based on the corresponding fraud suspicion values. The method additionally includes identifying one or more suspected devices and predicting a likelihood of account takeover from each of the one or more suspected devices. The method further includes detecting a triggering event from a device of the one or more suspected devices associated with an account. The method additionally includes executing a validation and/or remediation procedure based on a trigger sensitivity value associated with the triggering event, a respective likelihood of account takeover from the device associated with the account, a respective device risk value associated with the device, and a respective fraud suspicion value associated with the account.

Abstract: Techniques for adaptive validation and remediation are described. In some embodiments, the method includes determining, for a plurality of media service accounts, corresponding fraud suspicion values based on a model. The method also includes identifying a plurality of suspected accounts based on the corresponding fraud suspicion values. The method additionally includes identifying one or more suspected devices and predicting a likelihood of account takeover from each of the one or more suspected devices. The method further includes detecting a triggering event from a device of the one or more suspected devices associated with an account. The method additionally includes executing a validation and/or remediation procedure based on a trigger sensitivity value associated with the triggering event, a respective likelihood of account takeover from the device associated with the account, a respective device risk value associated with the device, and a respective fraud suspicion value associated with the account.

Abstract: In one illustrative example, TV usage data associated with a residential set-top box (STB) are obtained and stored over a plurality of repeated (e.g. weekly) time periods. TV usage pattern data associated with the residential STB are derived based on the stored TV usage data. A suspected illegitimate use of the residential STB is identified based on identifying a match or correlation between the TV usage pattern data and one of a plurality of predetermined commercial TV usage patterns. Further, for each listing data of one or more published listings of business hours, types, and themes associated with one or more commercial establishments, a comparison or correlation is performed between the TV usage pattern data and the listing data. A commercial establishment associated with illegitimate use is identified based on identifying a match or correlation between the TV usage pattern data and the listing data associated with the commercial establishment.

Abstract: In one illustrative example, TV usage data associated with a residential set-top box (STB) are obtained and stored over a plurality of repeated (e.g. weekly) time periods. TV usage pattern data associated with the residential STB are derived based on the stored TV usage data. A suspected illegitimate use of the residential STB is identified based on identifying a match or correlation between the TV usage pattern data and one of a plurality of predetermined commercial TV usage patterns. Further, for each listing data of one or more published listings of business hours, types, and themes associated with one or more commercial establishments, a comparison or correlation is performed between the TV usage pattern data and the listing data. A commercial establishment associated with illegitimate use is identified based on identifying a match or correlation between the TV usage pattern data and the listing data associated with the commercial establishment.

Abstract: In one illustrative example, TV usage data associated with a residential set-top box (STB) are obtained and stored over a plurality of repeated (e.g. weekly) time periods. TV usage pattern data associated with the residential STB are derived based on the stored TV usage data. A suspected illegitimate use of the residential STB is identified based on identifying a match or correlation between the TV usage pattern data and one of a plurality of predetermined commercial TV usage patterns. Further, for each listing data of one or more published listings of business hours, types, and themes associated with one or more commercial establishments, a comparison or correlation is performed between the TV usage pattern data and the listing data. A commercial establishment associated with illegitimate use is identified based on identifying a match or correlation between the TV usage pattern data and the listing data associated with the commercial establishment.

Abstract: In one illustrative example, TV usage data associated with a residential set-top box (STB) are obtained and stored over a plurality of repeated (e.g. weekly) time periods. TV usage pattern data associated with the residential STB are derived based on the stored TV usage data. A suspected illegitimate use of the residential STB is identified based on identifying a match or correlation between the TV usage pattern data and one of a plurality of predetermined commercial TV usage patterns. Further, for each listing data of one or more published listings of business hours, types, and themes associated with one or more commercial establishments, a comparison or correlation is performed between the TV usage pattern data and the listing data. A commercial establishment associated with illegitimate use is identified based on identifying a match or correlation between the TV usage pattern data and the listing data associated with the commercial establishment.