Google Inc.

Panayiotis Mavrommatis

Niels Provos

Yunkai Zhou

Mark Matthew Palatucci

Clayton Woodward Bavor, Jr

Kamal P Nigam

Eric L Davis

Christopher K Monson

Rachel Nakauchi

Ashish Bhatia

Min Gyung Kang

Monirul Islam Sharif

Sruthi Bandhakavi

Clayton W Bavor Jr

Jr Clayton W Bavor

Intrusion features of a landing page associated with sponsored content are identified. A feature score for the landing page based on the identified intrusion features is generated, and if the feature score for the landing page exceeds a feature threshold, the landing page is classified as a candidate landing page. A sponsor account associated with the candidate landing page can be suspended, or sponsored content associated with the candidate landing page can be suspended.

Detection of malware features in a content item

Intrusive software management

A method includes receiving a plurality of trusted assets, generating a first signature set for a known software application, and generating a second signature set for a subject software application. Each trusted asset is associated with at least a threshold number of trusted authors. Each signature in the first signature set corresponds to a known asset that is associated with the known software application. Each signature in the second signature set corresponds to a subject asset that is associated with the subject software application. The method further includes generating first and second filtered signature set based on the first and second signature sets, respectively, by excluding signatures corresponding to the trusted assets. The method also includes generating a similarity rating for the subject application based on a comparison of the first filtered signature set and the second filtered signature set.

Detecting pirated applications

Detecting an intrusive landing page

Landing pages associated with advertisements are partitioned into training landing pages and testing landing pages. Iterative training and testing of a classification mode on intrusion features of the partitioned landing pages is conducted until the occurrence of a cessation event. Feature weights are derived from the iterative training and testing, and are associated with the intrusion features. The associated feature weights and intrusion features can be used to classify other landing pages.

Intrusive feature classification model

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Mountain View, CA, United States of America

Panayiotis Mavrommatis