Alessandro Mantovani (EURECOM), Simone Aonzo (University of Genoa), Xabier Ugarte-Pedrero (Cisco Systems), Alessio Merlo (University of Genoa), Davide Balzarotti (EURECOM)

An open research problem on malware analysis is how to statically distinguish between packed and non-packed executables. This has an impact on antivirus software and malware analysis systems, which may need to apply different heuristics or to resort to more costly code emulation solutions to deal with the presence of potential packing routines. It can also affect the results of many research studies in which the authors adopt algorithms that are specifically designed for packed or non-packed binaries.

Therefore, a wrong answer to the question emph{``is this executable packed?''} can make the difference between malware evasion and detection. It has long been known that packing and entropy are strongly correlated, often leading to the wrong assumption that a low entropy score implies that an executable is NOT packed. Exceptions to this rule exist, but they have always been considered as one-off cases, with a negligible impact on any large scale experiment. However, if such assumption might have been acceptable in the past, our experiments show that this is not the case anymore as an increasing and remarkable number of packed malware samples implement proper schemes to keep their entropy low. In this paper, we empirically investigate and measure this problem by analyzing a dataset of 50K low-entropy Windows malware samples.

Our tests show that, despite all samples have a low entropy value, over 30% of them adopt some form of runtime packing. We then extended our analysis beyond the pure entropy, by considering all static features that have been proposed so far to identify packed code. Again, our tests show that even a state of the art machine learning classifier is unable to conclude whether a low-entropy sample is packed or not by relying only on features extracted with static analysis.

View More Papers

OcuLock: Exploring Human Visual System for Authentication in Virtual...

Shiqing Luo (Georgia State University), Anh Nguyen (Georgia State University), Chen Song (San Diego State University), Feng Lin (Zhejiang University), Wenyao Xu (SUNY Buffalo), Zhisheng Yan (Georgia State University)

Read More

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Harsh Chaudhari (Indian Institute of Science, Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (Indian Institute of Science, Bangalore)

Read More

IMP4GT: IMPersonation Attacks in 4G NeTworks

David Rupprecht (Ruhr University Bochum), Katharina Kohls (Ruhr University Bochum), Thorsten Holz (Ruhr University Bochum), Christina Poepper (NYU Abu Dhabi)

Read More

Revisiting Leakage Abuse Attacks

Laura Blackstone (Brown University), Seny Kamara (Brown University), Tarik Moataz (Brown University)

Read More