Dell Products Inc.

Tanuj Arcot Omkar

Cristiano André Da Costa

Vinicius Facco Rodrigues

Rodrigo Da Rosa Righi

Rodrigo De Souza Scorsatto

Rodolfo Stoffel Antunes

Lucas Micol Policarpo

Jorge Luis Victória Barbosa

Prashanth Coimbatore Srinivasan

Jonathan Leventis

Thaynã Da Silva França

Kasey Mallette

Surendhar Rajahram

Aravind Reddy Lakkadi

Methods, apparatus, and processor-readable storage media for detection of anomalous behavior on online platforms using machine learning techniques are provided herein. An example method includes obtaining a set of machine learning models configured to detect anomalous behavior associated with users interacting with an online platform and performing an incremental machine learning process on one or more of the machine learning models in the set. The incremental machine learning process may include obtaining data related to interactions of users with the online platform, updating at least one of the machine learning models in the set based on the obtained data, comparing the machine learning models, and selecting one of the machine learning models from the set to be used by the online platform based on the comparison. The method may further include determining, utilizing the selected machine learning model, that a given user is exhibiting anomalous behavior on the online platform.

Detection of anomalous behavior on online platforms using machine learning techniques

An apparatus comprises a processing device configured to monitor for events associated with users interacting with an e-commerce platform, to identify an event type of a given event associated with a given user interacting with the e-commerce platform, and to select, based on the identified event type, at least one of a plurality of machine learning models configured to characterize different types of potentially malicious behavior on the e-commerce platform. The processing device is also configured to determine, utilizing the selected at least one machine learning model, whether the given user is exhibiting at least one of the different types of potentially malicious behavior. The processing device is also configured, responsive to determining that the given user is exhibiting at least one of the different types of potentially malicious behavior, to initiate actions on the e-commerce platform to prevent or mitigate an effect of the potentially malicious behavior.

Machine learning-based detection of potentially malicious behavior on an e-commerce platform

Disclosed herein are methods, systems, and processes to track and whitelist third-party domains. A web request is received from a computing device. A Content Security Policy (CSP) header set is generated based on rule conditions and transmitted to the computing device. A violation notice indicating that third party domains used to render the web request violate one or more rule conditions identified by the CSP header set is received from the computing device. The third party domains are blocked as part of performing the web request.

Tracking and whitelisting third-party domains

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Cedar Park, TX, United States of America

Tanuj Arcot Omkar