Dell Products Inc.

Cristiano André Da Costa

Rodrigo Da Rosa Righi

Vinicius Facco Rodrigues

Tanuj Arcot Omkar

Rodolfo Stoffel Antunes

Jorge Luis Victória Barbosa

Rodrigo De Souza Scorsatto

Lucas Micol Policarpo

Rodrigo F Mohr

Luiz Ricardo Bertoldi De Oliveira

Marcio F S Lena

Jonathan Leventis

Thaynã Da Silva França

Kasey Mallette

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

A management server includes linear regression circuitry to receive the data as input data, based on the input data, the linear regression circuitry generates a linear regression line for the input data. Gradient circuitry analyzes angles between two points of data within the input data. In response to a first angle between a first point of data and a second point of data being greater than a threshold angle, the gradient circuitry determines that an interval of data including the first and second points of data is an abrupt interval, removes first data within the abrupt interval from the input data, and updates the linear regression line accordingly. Support vector machine circuitry receives the input data, classifies outliers in the input data, and removes the outliers. The linear regression line is updated based on the removal of the outliers.

System and method for improving server usage based on anomaly detection

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Porto Alegre, Brazil

Cristiano André Da Costa