Abstract: The present invention relates to a method and system for monitoring webpages for detecting malicious contents. According to a preferred embodiment the method comprises A) providing a plurality of URLs provided by a subscriber, employing a crawler to visit a URL webpage of said plurality of URLs; B) retrieving an object from said URL webpage by said crawler; C) analyzing said object retrieved by said crawler from said URL webpage, and determining whether said object retrieved is malicious or not; and D) alerting the subscriber, when said retrieved object is deemed malicious. According to one embodiment, the method further comprises E) employing a crawler to visit a URL webpage of a following URL of the plurality of URLs, when the determination of step C) is deemed not malicious; and F) returning to step B).

Abstract: A method for detecting an occurrence of an online event including retrieving a data item from online sources; forming a list by tagging words and/or strings within the data item according to predefined attributes such that the list includes the words and/or strings with their corresponding attributes; forming sequence items relating to the list according to a predefined criterion such that each sequence item includes at least the list and optionally additional preformed lists that have been formed in the same manner as the list and that have a shared concept with the list according to the predefined criterion; running each of the sequence items in a preformed machine learning classifying model that outputs a determination if there is an occurrence of the online event or not. The present invention further relates to generating the machine learning classifying model. The present invention also relates to a corresponding system.

Abstract: The present invention relates to a method and system for monitoring webpages for detecting malicious contents. According to a preferred embodiment the method comprises A) providing a plurality of URLs provided by a subscriber, employing a crawler to visit a URL webpage of said plurality of URLs; B) retrieving an object from said URL webpage by said crawler; C) analyzing said object retrieved by said crawler from said URL webpage, and determining whether said object retrieved is malicious or not; and D) alerting the subscriber, when said retrieved object is deemed malicious. According to one embodiment, the method further comprises E) employing a crawler to visit a URL webpage of a following URL of the plurality of URLs, when the determination of step C) is deemed not malicious; and F) returning to step B).

Abstract: A method for detecting an occurrence of an online event including retrieving a data item from online sources; forming a list by tagging words and/or strings within the data item according to predefined attributes such that the list includes the words and/or strings with their corresponding attributes; forming sequence items relating to the list according to a predefined criterion such that each sequence item includes at least the list and optionally additional preformed lists that have been formed in the same manner as the list and that have a shared concept with the list according to the predefined criterion; running each of the sequence items in a preformed machine learning classifying model that outputs a determination if there is an occurrence of the online event or not. The present invention further relates to generating the machine learning classifying model. The present invention also relates to a corresponding system.