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Bee master: Detecting host-based code injection attacks

 
: Barabosch, T.; Eschweiler, S.; Gerhards-Padilla, E.

:

Dietrich, S.:
Detection of intrusions and malware, and vulnerability assessment. 11th international conference, DIMVA 2014 : Egham, UK, July 10 - 11, 2014; Proceedings
Cham: Springer International Publishing, 2014 (Lecture Notes in Computer Science 8550)
ISBN: 978-3-319-08508-1 (Print)
ISBN: 978-3-319-08509-8 (Online)
S.235-254
International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) <11, 2014, Egham>
Englisch
Konferenzbeitrag
Fraunhofer FKIE ()

Abstract
A technique commonly used by malware for hiding on a targeted system is the host-based code injection attack. It allows malware to execute its code in a foreign process space enabling it to operate covertly and access critical information of other processes. Since there exists a plethora of different ways for injecting and executing code in a foreign process space, a generic approach spanning all these possibilities is needed. Approaches just focussing on low-level operating system details (e.g. API hooking) do not suffice since the suspicious API set is constantly extended. Thus, approaches focussing on low level operating system details are prone to miss novel attacks. Furthermore, such approaches are restricted to intimate knowledge of exactly one operating system. In this paper, we present Bee Master, a novel approach for detecting host-based code injection attacks. Bee Master applies the honeypot paradigm to OS processes and by that it does not rely on low-level OS details. The basic idea is to expose regular OS processes as a decoy to malware. Our approach focuses on concepts - such as threads or memory pages - present in every modern operating system. Therefore, Bee Master does not suffer from the drawbacks of low-level OS-based approaches. Furthermore, it allows OS independent detection of host-based code injection attacks. To test the capabilities of our approach, we evaluated Bee Master qualitatively and quantitatively on Microsoft Windows and Linux. The results show that it reaches reliable and robust detection for various current malware families.

: http://publica.fraunhofer.de/dokumente/N-301985.html