1. Abstraction Interpretation Abstract Interpretation is a general theory for approximating the semantics of dynamic systems (Cousot & Cousot 1977) Abstract Interpretation is a general theory for approximating the semantics of dynamic systems (Cousot & Cousot 1977) Computing means Interpreting For large/real programs control/data flow is too complex for being understandable by humans: Reverse Engineering needs abstraction! Reverse Engineering needs automated tools!

2. More Concrete observation More Abstract observation Modeling the Adversary: Degrees of abstraction

3. P We can quantify the security achieved by looking at proof complexity! key Proof Reverse Engineering is Interpreting Each tool is an Abstract Interpretation

4. O(P)O(P) Removing noise means refining abstractions / complicating proofs! (Giacobazzi et al 2000 / 2012) Proof Tracing Monitoring Slicing Profiling Decompiler Disassembler Static Analysis Dynamic Analysis SAT VMware SMT BinDiff BinHunt BinJuice HexRays GDB OllyDbg IDA Pro Theorem Prover Constrained Adversary Concolic Emulation Protecting is obscuring Interpretation Transform code to make all tools blind

5. Proof complexity Low High Degree of obfuscation Low Measuring Adversary Strength By constraining the adversary within a theorem prover we can quantify the security achieved from obfuscation Force the attacker to use automated tools (programs of large size and highly interconnected) Design code transformations making tools blind Determine lower bounds for proof complexity in obfuscated code Measure the degree of noise/slowdown induced in obfuscation