Methods and systems for generating incident recommendations in processed data using machine learning models. Specifically, the methods and system extract telemetry data from native game data. By reducing game data to telemetry data only, the system may reduce the amount of data that must be stored and processed. The system reconstructs game data in order to generate an incident recommendation. This reconstructed data allows for manual user review and/or understanding the context of an incident.

An electronic device and a method for detecting abnormal device operation are provided. The method includes: obtaining multiple action events of a movable input device, and each action event including a relative coordinate and a time stamp of the movable input device; generating multiple absolute coordinates based on the relative coordinate of each action event; estimating multiple speed vectors based on the absolute coordinates and the time stamp of each action event; estimating multiple acceleration vectors based on the speed vectors and the time stamp of each action event; and estimating a probability of abnormal operation based on the speed vectors and the acceleration vectors.

A target account detection method and apparatus, an electronic device, and a storage medium. The method includes: determining an active behavior timing feature of a target account to be detected according to active behavior data of the target account; determining an account feature of the target account according to account data of the target account; predicting a first probability that the target account is of a target type; and determining, in response to the first probability being greater than a target probability threshold, that the target account is of the target type. Detection is performed in the dimension of timing, so that the impact of an account of a target type pretending to be a normal account on detection can be reduced, and more accounts of the target type can be detected, thereby enlarging the identification coverage.

A user interaction monitoring system for monitoring a user's interactions with other users in an online environment includes: an interaction monitoring unit operable to monitor interactions between a first user and at least a first group of other users in the online environment and characterising the monitored interactions, an interaction identifying unit operable to identify whether interactions between the first user and the first group of other users deviate from the interactions between a second user and a second group of other users in the online environment, and a user experience modifying unit operable to modify the user experience in the online environment for the first user if it is identified that there is a deviation in the interactions.

Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Systems and methods are described herein for monitoring a gameplay session for violations of a policy and creating a remediation gameplay session through which remediation can be provided to players or player accounts that violate gameplay policies. The systems and methods can create a remediation gameplay session based in part on the game state data of the gameplay session during which the violation occurs.

Techniques for countering cheats in a multi-player gaming environment are described herein. In accordance with various embodiments, a server includes a cheating analyzer running on processor(s) and a non-transitory memory for storing cheat data and counter cheat data. The server identifies suspected cheating in a game and determines a probability of the suspected cheating based on the cheat data. The server also selects a counter cheat of the suspected cheating corresponding to the probability value based on the cheat data and the counter cheat data. The server then applies the counter cheat approximate the probability value in the game. In some embodiments, the server deploys local copies of the cheating analyzer to client devices, including sending at least a portion of the cheat data and/or counter cheat data. The local copies facilitate the cheat identification, the probability determination, the counter cheat selection, and/or the application of the counter cheat.

Methods and systems for filtering content in reconstructions of native data in assets. Specifically, the methods and systems extract telemetry data from native game data. The methods and systems then identify Personally Identifiable Information (“PII”) in the telemetry data and determine a masking operation to perform on the PII. The methods and systems then generate for presentation, in a user interface, reconstructed asset data based on the telemetry data, wherein the PII is modified in the reconstructed asset data based on the masking operation.

Apparatus and method for personalising participation in an activity comprising a plurality of user nodes, each user node including a sensor array serving to generate data in relation to a user at the node and a data processing device for receiving the generated data from the sensor array as to the user, the apparatus further comprising a computer system in communication with the sensor array in each node. This ensures a fluent and immediate personalised experience across the different user nodes.

Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.