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RuhrSec 2016: "Automatic Extraction of Indicators of Compromise for Web App.", Marco Balduzzi

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Published on May 10, 2016

Abstract. Indicators of Compromise (IOCs) are forensic artifacts that are used as signs that a system has been compromised by an attack or that it has been infected with a particular malicious software. In this paper we propose for the first time an automated technique to extract and validate IOCs for web applications, by analyzing the information collected by a high-interaction honeypot. Our approach has several advantages compared with traditional techniques used to detect malicious websites. First of all, not all the compromised web pages are malicious or harmful for the user. Some may be defaced to advertise product or services, and some may be part of affiliate programs to redirect users toward (more or less legitimate) online shopping websites. In any case, it is important to detect those pages to inform their owners and to alert the users on the fact that the content of the page has been compromised and cannot be trusted. Also in the case of more traditional drive-by-download pages, the use of IOCs allows for a prompt detection and correlation of infected pages, even before they may be blocked by more traditional URLs blacklists. Our experiments show that our system is able to automatically generate web indicators of compromise that have been used by attackers for several months (and sometimes years) in the wild without being detected. So far, these apparently harmless scripts were able to stay under the radar of the existing detection methodologies – resisting for long time on public web sites.

Biography. Marco Balduzzi holds a Ph.D. in applied IT security from Télécom ParisTech and a M.Sc. in computer engineering from the University of Bergamo. His interests concern all aspect of computer security, with particular emphasis on real problems that affect systems and networks. Some topics on which he worked on are web and browser security, code analysis, botnets detection, cybercrime investigation, privacy and threats in social networks, malware and intrusion detection systems.

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