Code obfuscation

Oct 15, 2014Download as PPTX, PDF1 like1,651 views

This document discusses code obfuscation techniques used to protect intellectual property and hide malicious code. It describes general obfuscation methods like control transformations, computation transformations, and data abstraction obfuscation. Specific techniques are outlined like opaque predicates, inheritance modifications, variable splitting, and method inlining/cloning. Code obfuscation aims to complicate reverse engineering but cannot completely prevent it, and it decreases performance. When used judiciously, obfuscation provides some protection against theft and malware insertion.

“ Code obfuscation is the practice of
making code unintelligible , or at the
very least, hard to understand ”
“General code obfuscation techniques
aim to confuse the understanding of
the way in which program functions.”

Who use the Code Obfuscation?
 code obfuscation is used to protect
intellectual property by software
companies.
 it is also used extensively by authors
of malicious code to avoid detection
from virus scanner.

Obfuscation Quality
 Potency
 Resilience
 Stealth
 Cost

Obfuscating control
transformations
 Aggregation transformation
 Control ordering transformations
 Computation Transformation

Computation Transformation
As the number of predicates increase in a body of code, insertion of
dead or irrelevant code into the program becomes easier.
Inserting Opaque Predicate

Computation Transformation
obfuscate a loop

Obfuscating data abstractions
 Modifying inheritance relations
 Restructure Arrays
 Obfuscating Procedural Abstractions
 Inline and Outline Methods
 Clone Methods
 Obfuscating built-in data types
 Split variables
 Convert static to procedural data
 Merge scalar variables

Modifying inheritance relations
The complexity of a program increases with
greater depth of the inheritance tree.
Along these lines, we can artificially increase the
complexity of a program
 Increasing Depth of Inheritance
 Introducing Bogus Classes

Merge Scalar Variables
 This method of obfuscation involves merging two
or more scalar variables into a single variable.
 The variables v1, v2 . . . vk can be merged into one
variable Vm provided the the combined ranges of
v1, v2 . . . vk fit within the precision of Vm.

Converting Static Data to
Procedural Data

Split Variables
 Variables of restricted range can be split up into two or
more variables.
 In order to split a variable V of type T into two variables
p and q of type U,
 1. A function f(p,q) that maps the values of p and q into
the corresponding value of V.
 2. a function g(V) that maps the value of V into the
corresponding values of p and q.
 3. new operations cast in terms of operationson p and
q.

Clone Methods
ADD()
{
int a=1,b=2,c;
c=a+b;
}
---------------------------------
ADDITION()
{
int a=1,b=2,c;
c=a+b;
}
----------------------------------
AddTwoNumber()
{
int a=1,b=2,c;
c=a+b;
}
Program :
{
Add();
.
.
Addition();
.
.
.
AddTowNumber();
}

Code obfuscation by obstructing
static analysis of programs
 Control-flow Transformations
 Data-flow Transformations

Code Obfuscation in
Disassembly Phase
 Thwarting disassembly
 Junk Insertion
 Thwarting Linear Sweep
 Thwarting Recursive Traversal
 Branch functions
 Call conversion
 Opaque predicates
 Jump Table Spoofing

Code Obfuscation as it Relates
to Viruses

Code Obfuscation as it Relates
to Viruses
 Virus Types
 Polymorphic
 Metamorphic
 Obfuscation Techniques
 Dead Code Insertion
 Code Transposition
 Register Reassignment
 Instruction Substitution
 Comparisons
 Another Angle

Conclusion
 No obfuscation has yet been found that can
completely resist reverse engineering.
 Code obfuscation increases the code decreases
performance, and can hinder certain compiler
optimizations.
 when used sparingly, and combined
appropriately, can add a layer of protection
against theft and insertion of malicious code

How much Attention should be
given to Software Protection?
Software
Users
Group 1
True Client
Who buy
software
license.
Group 2
Client ,
Programmers.
Group 3
Professional
Hackers.

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Enabling interfaces to declare (instance) method implementations, Java 8 default methods can be used as a substitute for the ubiquitous skeletal implementation software design pattern. Performing this transformation on legacy software manually, though, may be non-trivial. The refactoring requires analyzing complex type hierarchies, resolving multiple implementation inheritance issues, reconciling differences between class and interface methods, and analyzing tie-breakers (dispatch precedence) with overriding class methods. All of this is necessary to preserve type-correctness and confirm semantics preservation. We demonstrate an automated refactoring tool called MIGRATE SKELETAL IMPLEMENTATION TO INTERFACE for transforming legacy Java code to use the new default construct. The tool, implemented as an Eclipse plug-in, is driven by an efficient, fully-automated, type constraint-based refactoring approach. It features an extensive ruleset covering various corner-cases where default methods cannot be used. The resulting code is semantically equivalent to the original, more succinct, easier to comprehend, less complex, and exhibits increased modularity. A demonstration can be found at http://youtu.be/YZHIy0yePh8.

Improving Code Quality Through Effective Review ProcessDr. Syed Hassan Amin

The document discusses improving code quality through effective code review processes. It outlines common coding mistakes like redundant code, long or deeply nested functions, large modules, poor comments, and hardcoding. It recommends following best practices like coding guidelines, centralized server communication, and the single responsibility principle. The document also discusses measuring and reducing code complexity, avoiding memory leaks, optimizing images, static code analysis, and profiling to improve code quality.

De-virtualizing virtual Function Calls using various Type Analysis Technique...IOSR Journals

This document discusses techniques for optimizing virtual function calls in object-oriented programming languages. Virtual function calls are indirect calls that involve lookup through a virtual function table (VFT) at runtime, which has performance overhead compared to direct calls. Various static analysis techniques like Class Hierarchy Analysis (CHA) and Rapid Type Analysis (RTA) aim to resolve some virtual calls by determining the possible target types and replacing indirect calls with direct calls if a single target is possible. CHA uses the class hierarchy and declared types to determine possible target types, while RTA also considers instantiated types in the program to further reduce possible targets. The document analyzes examples to demonstrate how CHA and RTA can optimize some virtual calls.

Kroening et al, v2c a verilog to c translatorsce,bhopal

The document describes v2c, a tool that translates Verilog to C. v2c accepts synthesizable Verilog as input and generates equivalent C code called a "software netlist". The translation is based on Verilog's synthesis semantics and preserves cycle accuracy and bit precision. The generated C code can then be used for hardware property verification, co-verification, simulation, and equivalence checking by leveraging software verification techniques.

Measuring maintainability; software metrics explainedDennis de Greef

In a world of ever-changing business requirements, how can you keep your software moving at the same pace? If you keep adding lines of code around the previous iteration to add new functionality, things can become complex quite fast. By measuring complexity, you can resolve and prevent bugs, while measuring class responsibility can make refactoring easier, for example. In this talk Dennis will go through certain concepts of analysing software with automated tools to spit out numbers which tell a story about your code.

M017318288IOSR Journals

This document summarizes a novel algorithm to protect and manage memory locations through comprehensive randomization. The algorithm randomizes both the absolute locations of all code and data objects as well as their relative distances. This is achieved through a source-to-source transformation that produces a self-randomizing program. The transformation separates buffer variables that are more prone to errors from other variables and introduces inaccessible memory pages before and after buffers. It also randomizes the order of functions and inserts random gaps between them. The goal is to ensure any invalid memory access results are unpredictable to prevent exploits while maintaining low runtime overhead.

A novel algorithm to protect and manage memory locationsiosrjce

IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.

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The document provides an overview of functional programming in JavaScript. It discusses key functional programming concepts like pure functions, referential transparency, and higher-order functions. It also covers functional techniques like mapping, filtering, reducing, and recursion that are commonly used in functional programming. The document uses examples with Lodash functions to demonstrate how these concepts and techniques can be implemented in JavaScript.

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This document discusses object-oriented programming concepts like classes, objects, encapsulation, inheritance, polymorphism, and more. It provides examples of defining classes with data members and member functions, creating objects, passing objects as arguments, and using constructors and destructors. Key points include how memory is allocated for classes and objects, characteristics of constructors, constructor overloading, and examples of programs using constructors and destructors to print student details.

VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assis...Stefano Dalla Palma

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Dynamic Multi Levels Java Code Obfuscation Technique (DMLJCOT)CSCJournals

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IEEE 2015 Java ProjectsVijay Karan

Defaultification Refactoring: A Tool for Automatically Converting Java Method...Raffi Khatchadourian

Improving Code Quality Through Effective Review ProcessDr. Syed Hassan Amin

De-virtualizing virtual Function Calls using various Type Analysis Technique...IOSR Journals

Kroening et al, v2c a verilog to c translatorsce,bhopal

Measuring maintainability; software metrics explainedDennis de Greef

M017318288IOSR Journals

A novel algorithm to protect and manage memory locationsiosrjce

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Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri

AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions

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Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev

If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic? Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search. Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval. GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.

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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.

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Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe. We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved? Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security. Talk from SciFiDevCon 2025 https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5

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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

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