Adversarial Machine Learning for the Protection of Legitimate Software

Obfuscation is the transforming a given program into one that is syntactically different but semantically equivalent. This new obfuscated program now has its code and/or data changed so that they are hidden and difficult for attackers to understand. Obfuscation is an important security tool and used to defend against reverse engineering. When applied to a program, different transformations can be observed to exhibit differing degrees of complexity and changes to the program. Recent work has shown, by studying these side effects, one can associate patterns with different transformations. By taking this into account and attempting to profile these unique side effects, it is possible to create a classifier using machine learning which can analyze transformed software and identifies what transformation was used to put it in its current state. This has the effect of weakening the security of obfuscating transformations used to protect legitimate software. In this research, we explore options to increase the robustness of obfuscation against attackers who utilize machine learning, particular those who use it to identify the type of obfuscation being employed. To accomplish this, we segment our research into three stages. For the first stage, we implement a suite of classifiers that are used to xiv identify the obfuscation used in samples. These establish a baseline for determining the effectiveness of our proposed defenses and make use of three varied feature sets. For the second stage, we explore methods to evade detection by the classifiers. To accomplish this, attacks setup using the principles of adversarial machine learning are carried out as evasion attacks. These attacks take an obfuscated program and make subtle changes to various aspects that will cause it to be mislabeled by the classifiers. The changes made to the programs affect features looked at by our classifiers, focusing mainly on the number and distribution of opcodes within the program. A constraint of these changes is that the program remains semantically unchanged. In addition, we explore a means of algorithmic dead code insertion in to achieve comparable results against a broader range of classifiers. In the third stage, we combine our attack strategies and evaluate the effect of our changes on the strength of obfuscating transformations. We also propose a framework to implement and automate these and other measures. We the following contributions: 1. An evaluation of the effectiveness of supervised learning models at labeling obfuscated transformations. We create these models using three unique feature sets: Code Images, Opcode N-grams, and Gadgets. 2. Demonstration of two approaches to algorithmic dummy code insertion designed to improve the stealth of obfuscating transformations against machine learning: Adversarial Obfuscation and Opcode Expansion 3. A unified version of our two defenses capable of achieving effectiveness against a broad range of classifiers, while also demonstrating its impact on obfuscation metrics

Code obfuscation against abstraction refinement attacks

Code protection technologies require anti reverse engineering transformations to obfuscate programs in such a way that tools and methods for program analysis become ineffective. We introduce the concept of model deformation inducing an effective code obfuscation against attacks performed by abstract model checking. This means complicating the model in such a way a high number of spurious traces are generated in any formal verification of the property to disclose about the system under attack.We transform the program model in order to make the removal of spurious counterexamples by abstraction refinement maximally inefficient. Because our approach is intended to defeat the fundamental abstraction refinement strategy, we are independent from the specific attack carried out by abstract model checking. A measure of the quality of the obfuscation obtained by model deformation is given together with a corresponding best obfuscation strategy for abstract model checking based on partition refinement

Code deobfuscation by program synthesis-aided simplification of mixed boolean-arithmetic expressions

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Raúl Roca Cánovas, Antoni Benseny i Mario Reyes de los Mozos[en] This project studies the theoretical background of Mixed Boolean-Arithmetic (MBA) expressions as well as its practical applicability within the field of code obfuscation, which is a technique used both by malware threats and software protection in order to complicate the process of reverse engineering (parts of) a program. An MBA expression is composed of integer arithmetic operators, e.g. $(+,-, *)$ and bitwise operators, e.g. $(\wedge, \vee, \oplus, \neg).$ MBA expressions can be leveraged to obfuscate the data-flow of code by iteratively applying rewrite rules and function identities that complicate (obfuscate) the initial expression while preserving its semantic behavior. This possibility is motivated by the fact that the combination of operators from these different fields do not interact well together: we have no rules (distributivity, factorization...) or general theory to deal with this mixing of operators. Current deobfuscation techniques to address simplification of this type of data-flow obfuscation are limited by being strongly tied to syntactic complexity. We explore novel program synthesis approaches for addressing simplification of MBA expressions by reasoning on the semantics of the obfuscated expressions instead of syntax, discussing their applicability as well as their limits. We present our own tool $r$ 2syntia that integrates Syntia, an open source program synthesis tool, into the reverse engineering framework radare 2 in order to retrieve the semantics of obfuscated code from its Input/Output behavior. Finally, we provide some improvement ideas and potential areas for future work to be done