1. Impeding Malware Analysis Using Conditional Code Obfuscation Paper by: Monirul Sharif, Andrea Lanzi, Jonathon Giffin, and Wenke Lee Conference: Network and Distributed System Security Symposium (NDSS), 2008 Presented by: LIU Limin

2. Outline Introduction Conditional Code Obfuscation Implications Implementation and Evaluation Discussion

3. Introduction Hundreds of new malware samples appear every day. –Trojans, Rootkits, Worms, Viruses, Backdoors … Automated malware analysis becomes increasingly important. –Static analysis –Dynamic analysis –State-of-the-art analyzer

4. Malware Analysis Offense –Polymorphism, metamorphism and opaque predicates. –Trigger based behavior. (time- bombs, logic- bombs, bot commands etc.) ? Defense –Static analysis –Dynamic analysis –Input-oblivious analyzers (Dynamic multiple path exploration, Forced execution)

5. Obfuscation Obfuscations that are easily to be applicable on existing code can be a threat. Conditional Code Obfuscation: A simple, automated and transparent obfuscation against powerful input- oblivious analyzer.

6. Outline Introduction Conditional Code Obfuscation Implications Implementation And Evaluation Discussion

7. Conditional Code Snippets cmd = get_command (sock); if (strcmp (cmd, “startkeylogger”) == 0) { log_keys(); } n = get_day_of_month (); if ((n > 10) && (n<20)) { attack(); } E.g.1 E.g.2

8. Obfuscated example snippet Original code Obfuscated code cmd = get_command (sock); if (hash (cmd) == H) /* here, H= hash(“startkeylogger”)*/ { decrypt_function (encr_log_keys, cmd); encr_log_keys(); /*encrypted log_keys*/ } cmd = get_command (sock); if (strcmp (cmd, “startkeylogger”) == 0) { log_keys(); } One-way

9. General Obfuscation Mechanism Hash properties –Pre-image resistance: infeasible to find c given H c. –Second pre-image resistance: hard to find another c’ for which Hash (c’) = H c. Candidate conditions –Equality operators: ‘==’, strcmp, strncmp, memcmp… –Unsupported operators: ‘>’, ‘<’… Conditional code –Code that gets executed when a condition is satisfied.

10. Automation using Static Analysis Finding Conditional Code –Identify candidate conditions Construct a CFG for each function Identify basic blocks having conditional branches Select candidate conditions those contain equality operators –Find corresponding conditional code Intra-procedural: basic blocks which are control dependent on condition with true outcome Inter-procedural: set of functions which only be reachable when certain condition is satisfied

11. Automation using Static Analysis Handling Common Conditional Code –Duplicate the code and encrypt it separately for each candidate condition.

12. Simplifying Compound Constructs Operators (&& or ||…) combine more than one simple condition Break the compound conditions into semantically equivalent but simplified conditions

13. Outline Introduction Conditional Code Obfuscation Implications Implementation And Evaluation Discussion

14. Consequences to Existing Analyzers Path exploration and input discovery –Construct constraints for each path (e.g. X == c ). Input Discovery (EXE) –Discover inputs from constraints by using symbolic execution. Obfuscated constraints is “Hash (X) == H c ” Infeasible to reverse the hash function.

15. Consequences to Existing Analyzers Forcing execution –Force execution along a specific path without solving the constraints –Without key, program crashes. Static analysis –Conceal the behavior in the encrypted block

16. Attacks Brute Force and Dictionary Attacks –Constraint: Hash (X) = H c Find possible X for satisfying above equation. Domain (X) : set of all possible values that X may take during execution. t: time taken to a test a single value of X or the hash computation time. Brute Force attempt: time = |Domain (X)|* t. If X is n bits in length, attack requires 2 n t time.

17. Outline Introduction Conditional Code Obfuscation Implications Implementation And Evaluation Discussion

18. Implementation Platform: Linux Input: C/C++ Source; Output: ELF Binary Four phases: –Front-end Code Parsing Phase –Analysis/Transformation Phase –Code Generation Phase –Encryption Phase Two Levels: –Binary level: decrypted code is executable –Intermediate code level: data types information

19. Analysis phase Candidate Condition Replacement –Identify candidate conditions and their conditional code –Hash function: SHA-256 Decipher Routine –Encryption algorithm: AES with 256-bit keys Decryption Key and Markers –Key (X) = Hash (X|N), N is a nonce. –marker: foresee the exact location of the corresponding code in the resulting binary file.

20. Encryption phase Identify code blocks needing encryption. Extracts the encryption key K c. Replace K c and End_marker() with NOP instructions. Calculate the size of the block to be encrypted. Place the size as argument to the call to Decipher. Encrypt the block with the key K c.

21. Experimental Evaluation Evaluate system by determining how many manually identified trigger-based malicious behavior were automatically and completed obfuscated. Three levels of obfuscation strength: –Strong: strings –Medium: integers –Weak: boolean flags

22. Outline Introduction Conditional Code Obfuscation Implications Implementation And Evaluation Discussion

23. Strengths Malware author can modify the programs to improve the strengths. –Introducing more candidate conditions. Query for resources and compare with the names. Replace operators such as or != by ==. –Increasing the size of the concealed code. Incorporate triggers that encapsulates more execution behavior. –Increasing the input domains. Use variables with larger domains (e.g., string) or use integer with larger size.

24. Weakness Limited types of conditions –Equality checks. Input domain may be very small in some cases. –32-bit or 64-bit integers.

25. Possible ways to defeat Equipped with decryptors that reduce the search space of keys by taking the input domain into account. –the result or an argument receiving data from a system call, e.g. gettimeofday. Input-aware analysis. –Collection mechanisms capture interaction of the binary with its environment.

26. Conclusion An obfuscation scheme that can be automatically applied on malware programs. The obfuscation conceal trigger based-malicious behavior from state-of-the-art malware analyzers. It is shown that the obfuscation scheme is capable of concealing a large fraction of malicious triggers by experiment.