Computer security method for the analysis of passive traces of HTTP and HTTPS traffic on the Internet, with extraction and grouping of similar Web transactions automatically generated by malware, malicious services, unsolicited advertising or other, comprises at least the following processing and control steps: a) URLs extraction from an operational network, using passive exploration of the HTTP e HTTPS traffic data and subsequent collection into batches of the extracted URLs; b) detection of similar URLs, by metrics calculation based on the distance among URLs, namely based on a measure of the degree of diversity among pairs of character strings composing the URLs; c) activation of one or more clustering algorithms used to group the URLs based on the similarity metrics and to obtain, within each group of URLs, elements with similar/homogeneous features, adapted to be analyzed as a single entity; d) visualization of elements according to a sorting based on the degree of cohesion of the URLs contained in each grouping.
A method for exploring traffic passive traces and grouping similar urls / Mellia, Marco; Metwalley, Hassan; Bocchi, Enrico; Morichetta, Andrea. - (2018).
A method for exploring traffic passive traces and grouping similar urls
MELLIA, Marco;METWALLEY, Hassan;BOCCHI, Enrico;MORICHETTA, Andrea
2018
Abstract
Computer security method for the analysis of passive traces of HTTP and HTTPS traffic on the Internet, with extraction and grouping of similar Web transactions automatically generated by malware, malicious services, unsolicited advertising or other, comprises at least the following processing and control steps: a) URLs extraction from an operational network, using passive exploration of the HTTP e HTTPS traffic data and subsequent collection into batches of the extracted URLs; b) detection of similar URLs, by metrics calculation based on the distance among URLs, namely based on a measure of the degree of diversity among pairs of character strings composing the URLs; c) activation of one or more clustering algorithms used to group the URLs based on the similarity metrics and to obtain, within each group of URLs, elements with similar/homogeneous features, adapted to be analyzed as a single entity; d) visualization of elements according to a sorting based on the degree of cohesion of the URLs contained in each grouping.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2860905