1. Eureka: A Framework for Enabling Static Malware Analysis the 13 th European Symposium on Research in Computer Security (ESORICS) conference 2008 WANG Zhi

2. Outline Overview of Generic Unpacker 1 System Call Level Heuristic 2 Statistics-Based Unpacking 3 Evaluation Metrics 4

3. Overview of Unpacker  Static analyses: decompile and analyze the logical structure, flow, and data stored within the binary itself.  Dynamic analyses: monitor the behavior of the malware binary at runtime.  Fine-grained monitor (Instruction-level)  Coarse-grained monitor (page-level)

4. Generic Automatic Unpackers PolyUnpackRenovoOmniUnpack Eureka Instruction-level Page-levelSystem call level Model-base trigger Heuristic trigger Heuristic and Statistical trigger slow fast  The variability in unpacking strategies come from the granularity of tracking unpacking behavior.

5. Eureka Coarse-grained execution tracing NtTerminateProcess NtCreateProcess Eureka Statistical bigram analysis bigram.

6. Coarse-grained Execution Tracing  Eureka uses the event of program exit as a trigger.  NtTerminateProcess implies that the unpacked malicious payload has been successfully decrypted.  A large fraction of current malware use a new process (NtCreateProcess) to execute the unpacked malicious payload.

7. Problems  Not all malware exit and keep an executing version resident in memory  Packers can make spurious event of creating new process.  Malware authors can simply avoid exiting the malware process.  The above two simple heuristics may work for a large fraction of malware today( as much as 80%), it may not be the same for future malware.

8. Evaluation

9. Statistical bigram analysis  Mining statistical patterns in x86 code  Use simple n-gram analysis  Use the IDA Pro to extract regions from executable that were marked as functions.  Looking for the most common bigrams ( opcode pairs or 2-byte opcodes) and space bigrams( byte pairs separated by 1 or more bytes)  Found FF 15(call), FF 75(push), E8---00 and E8---FF are prevalent in x86 code.

10. Occurrence summary of bigrams calcexplorernotepadpingshutdown FF 15(call)246304541558132 FF 75(push)23524942454185 E8---FF(call)158322011808749 E8---00(call)74610911085766

11. Bigram Counts  Bigram counts during execution of goat file packed with Aspack

12. Bigram Counts  Bigram counts during execution of goat file packed with Molbox

13. Bigram Counts  Bigram counts during execution of goat file packed with Armadillo

14. Bigram Counts  There are consistent and significant shifts in the bigram counts.  The simple bigram counting approach had over a 95% success rate in distinguishing between packed and unpacked malware instance.

15. Evaluation Metrics  Code-to-data ratio  An observable difference between packed code and unpacked code is the amount of identifiable code and data found in the binary  Use IDA Pro to identify valid code sequences.  In IDA Pro, data are represented by db, dw or dd.  In packed executables, the ratio is below 3%.  In unpacked executables, the ratio is above 50%.

16. Code-to-data ratio Packed Unpacked

17. Code-to-data ratio Grey area stand for data Blue area stand for code Packed notepad.exe memory space Original notepad.exe memory space