Abstract: Techniques are provided for assessing and determining risks that are posed by various users to a website. The determined risk for each user can be used to modify the user experience of the website in a manner that is commensurate with the risk (or non-risk) posed by the user. Assessment of risks posed a website guest can be performed by collecting guest-related data from a plurality of different service engines, aggregating contextual information from the guest-related data, and calculating a risk score based on the contextual information. The risk score can represent the internal reputation of the guest using the guest device. The risk score can be transmitted to a remote computing device and used to modify content of the website according to the risk score. Further, a recommended action can be determined based on the risk score, which can be taken by a backend service for the website guest.

Abstract: Techniques are provided for assessing and determining risks that are posed by various users to a website. The determined risk for each user can be used to modify the user experience of the website in a manner that is commensurate with the risk (or non-risk) posed by the user. Assessment of risks posed a website guest can be performed by collecting guest-related data from a plurality of different service engines, aggregating contextual information from the guest-related data, and calculating a risk score based on the contextual information. The risk score can represent the internal reputation of the guest using the guest device. The risk score can be transmitted to a remote computing device and used to modify content of the website according to the risk score. Further, a recommended action can be determined based on the risk score, which can be taken by a backend service for the website guest.

Abstract: In some implementations, a method performed by data processing apparatuses includes receiving a new script document in a scripting language that has not yet been classified; identifying features of the new script document, wherein at least some of the features are script-language commands contained in the new script document; generating first feature-data for the new script document, the first feature-data comprising measures of frequency of occurrences of the features within the new script document; and assigning a classification to the new script document based on a comparison of the first feature-data with training data that comprises second feature-data for known-malicious script documents and third feature-data for known-benign script documents.

Abstract: In some implementations, a method performed by data processing apparatuses includes receiving a new script document in a scripting language that has not yet been classified; identifying features of the new script document, wherein at least some of the features are script-language commands contained in the new script document; generating first feature-data for the new script document, the first feature-data comprising measures of frequency of occurrences of the features within the new script document; and assigning a classification to the new script document based on a comparison of the first feature-data with training data that comprises second feature-data for known-malicious script documents and third feature-data for known-benign script documents.