Abstract: A method and an electronic device for training a classifier to identify a user of the electronic device are provided. The method comprises: receiving training key stroke data associated with the user, a given portion of the training key stroke data having been generated in response to the user inputting a respective symbol of a predetermined text into the electronic device, by interacting with a given key of the plurality of keys; determining, based on the given portion of the training key stroke data, a plurality of time intervals associated with the user; determining most stable ones of the plurality of time intervals for inclusion thereof in a training set of data; training, based on the training set of data, the classifier to determine if the predetermined text inputted into the electronic device in future has been inputted by the user.

Abstract: A method and a system for detecting harmful content on a network are provided. The method comprises: receiving a URL; obtaining, from the URL, an HTML document associated therewith; converting the HTML document into a text; normalizing the text associated with the HTML document, thereby generating a plurality of tokens associated therewith; aggregating, each one of the plurality of tokens into a token vector associated with the HTML document; and applying, one or more classifiers to the token vector associated with the HTML document to determine a likelihood parameter indicative of the URL being associated with the harmful content; in response to the likelihood parameter being equal to or greater than a predetermined likelihood parameter threshold: identifying, the URL as being associated with the harmful content; and storing, the URL in a database of harmful URLs.

Abstract: There is disclosed a method for training a Machine Learning Algorithm (MLA) for classifying a user action sequence that is performed by a user with an electronic service on a computer device. The method comprises: receiving an indication of interface elements of the electronic service and events associated with the interface elements to be monitored; receiving a plurality of indications of the user action sequence, the plurality of indications being of at least two different types of classes, for which the MLA is to be trained for classifying user actions into; generating a training set by: subdividing the given user action sequence into subsequences, determining a frequency of each subsequence appearing in the user action sequences belonging to a given one of the at least two different types of classes; scoring each subsequence based on the frequency; selecting n most informative subsequences.

Abstract: There is disclosed a method of creating a template of original video content, which is performed on a computer device that has access to a previously generated database of original video content. The method comprises receiving identifiers for at least a portion of an original video content; extracting at least a portion of metadata of the original video content; extracting at least a portion of frames from a sequence of frames of the original video content; identifying a sequence of scenes; creating a vector of the sequence of scenes; generating a template of the original video content that includes at least the portion of the metadata, and a vector of the sequence of scenes of the original video content; and storing the template in a database.

Abstract: A method and a system for detecting an unauthorized activity at a user device are provided. The method comprises: analyzing a first request from the user device, the first request including original client cookie; in response to the original client cookie meeting a predetermined threshold: causing the user device to receive a Java Script Module, thereby enabling the user device to generate a second request, by: receiving server cookie indicative of a given activity associated with the user device being one of: a user activity and a bot activity; generating the second request including first client cookie and the server cookie; determining if the second request is to be transmitted to a web content server associated with the first web page; in response to the server cookie data being indicative of the bot activity: the second request is blocked.

Abstract: A method and a system for user identification of a user based on a current user environment of a user device thereof are provided. The method comprises: causing the user device to receive an analytical module for installation thereof in a web application, the analytical module being configured to execute: causing installation of an iframe container in a given page of the web application launched on the user device; retrieving at least one cookie file including user data indicative of the current user environment of the user device; transmitting the at least one cookie file to an identification server for modification thereof to generate an in-use cookie file; and receiving the in-use cookie file; analyzing the in-use cookie file for identifying, based on the user data, the user device, and in response to identifying the user device as being compromised, transmitting a predetermined notification to the application content server.

Abstract: The disclosed non-limiting embodiments of the present technology are directed to methods and systems for warning in regard to cyber threats, especially methods to detect malicious web resources, in particular phishing websites, and mirrors of blocked and/or prohibited websites. The disclosed method comprises receiving, by the computer device, pointers for a plurality of web resources; extracting, by the computer device, at least some of the content elements of each web resource of the plurality of web resources. Iteratively combining, by the computer device, at least two content elements into subgroups and then iteratively combining subgroups into groups in response to a number of web resources including the at least two content elements exceeding a predefined minimum threshold of web resources for the at least one first subgroup and/or group.

Abstract: There is disclosed a method to automatically identify fuzzy video content copies, which is performed on a computer device that has access to a previously generated database of original video content. The method comprises: receiving identifiers for video content; extracting each Nth frame from the video stream of received video content to obtain a sequence of frames, where N>1; identifying the sequence of scenes by comparing at least fragments of adjacent frames; creating a vector of the sequence of scenes of the received video content; comparing the vector of the sequence of scenes of received video content with the vector of the sequence of scenes of at least one template of original video content from a previously generated database of original video content; identifying a fuzzy copy of original video content when the sequence of scenes of original video content and scenes of received video content coincide to an extent that exceeds a predefined threshold.

Abstract: Non-limiting embodiments of the present technology are directed to a field of computer science, and particularly to the methods and systems for remote access detection when browsing web resource pages. A method comprises receiving data representative of a periodicity of a computer mouse movement events; generating a statistical model, the statistical model representative of a typical periodicity of the computer mouse movement events associated with a legitimate user of the electronic device; receiving an indication of computer mouse movement events from the electronic device during a browsing session of the web resource; comparing a periodicity of the computer mouse movement events with the statistical model; in response to detecting a deviation in computer mouse movement events, generating a notification determining a presence of a remote connection to the browsing session; transmitting the notification to an entity associated with the web resources.