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README.md
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---
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license: mit
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---
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---
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license: mit
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+
task_categories:
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- tabular-classification
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tags:
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- software
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size_categories:
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- 10K<n<100K
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---
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# FuzzDistill Dataset Explanation
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The final Basic Block and Function features are extracted in Semicolon-Separated Values (SSV) format.
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To load in pandas, use -
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```python
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import pandas as pd
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data = pd.read_csv("FNFeatures.csv", sep=";")
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```
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Assuming `FNFeatures.csv` is the target feature file.
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## Function Features
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The generated dataset is list of functions with various characteristics and a label indicating whether each function is
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vulnerable or not. The data is structured into 14 columns, which are described below:
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1. **Function ID**: A unique identifier for each function.
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2. **Function Name**: The name of the function.
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3. **Instructions**: The number of instructions (e.g.,Intermediate Instructions) in the function.
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4. **BBs** (Basic Blocks): The number of basic blocks in the function. A basic block is a sequence of instructions that
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are executed together without any control flow changes.
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5. **In-degree**: The number of incoming edges to the function in the call graph, indicating how many other functions
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call this one.
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6. **Out-degree**: The number of outgoing edges from the function in the call graph, indicating how many other functions
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are called by this one.
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7. **Num Loops**: The number of loops (e.g., for, while, do-while) present in the function.
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8. **Static Allocations**: The number of static memory allocations made by the function.
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9. **Dynamic Allocations**: The number of dynamic memory allocations made by the function (e.g., using `malloc`,
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`realloc`).
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10. **MemOps** (Memory Operations): The number of memory-related operations performed by the function (e.g., reads,
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writes).
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11. **CondBranches** (Conditional Branches): The number of conditional branches (e.g., if-else statements) in the
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function.
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12. **UnCondBranches** (Unconditional Branches): The number of unconditional branches (e.g., jumps, returns) in the
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function.
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13. **DirectCalls**: The number of direct function calls made by the function.
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14. **InDirectCalls** (Indirect Calls): The number of indirect function calls made by the function (e.g., through a
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pointer or a table).
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15. **VULNERABLE**: A binary label indicating whether the function is vulnerable (1) or not (0).
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## Basic Block Features
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Generated Basic Block dataset a collection of basic blocks (BBs) from functions with various characteristics and a label
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indicating whether each block is vulnerable or not. The data is structured into 13 columns, which are described below:
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1. **Block ID**: A unique identifier for each basic block.
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2. **Block Name**: Name of the block with following structure - `BB_<block #>_<demangled parent function>`
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3. **Instructions**: The number of instructions (e.g., assembly code operations) in the basic block.
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4. **In-degree**: The number of incoming edges to the basic block in the control flow graph, indicating how many other
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blocks lead to this one.
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5. **Out-degree**: The number of outgoing edges from the basic block in the control flow graph, indicating how many
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other blocks are reachable from this one.
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6. **Static Allocations**: The number of static memory allocations made by the basic block.
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7. **Dynamic Allocations**: The number of dynamic memory allocations made by the basic block (e.g., using `new`,
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`malloc`).
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8. **MemOps** (Memory Operations): The number of memory-related operations performed by the basic block (e.g., reads,
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writes).
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9. **CondBranches** (Conditional Branches): The number of conditional branches (e.g., if-else statements) in the basic
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block.
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10. **UnCondBranches** (Unconditional Branches): The number of unconditional branches (e.g., jumps, returns) in the
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basic block.
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11. **DirectCalls**: The number of direct function calls made by the basic block.
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12. **InDirectCalls** (Indirect Calls): The number of indirect function calls made by the basic block (e.g., through a
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pointer or a table).
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13. **VULNERABLE**: A binary label indicating whether the basic block is vulnerable (1) or not (0).
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---
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### A Note on Branches in Basic Blocks
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Conditional branches (CondBranches) and unconditional branches (UnCondBranches) primarily serve as sanity checks and do
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not significantly impact the categorization of Basic Blocks (it might actually harm the accuracy). Let’s analyze the
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possible values of \( N \) (number of conditional branches) and \( M \) (number of unconditional branches).
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A basic block can contain at most one conditional branch. A conditional branch is typically used to terminate the block
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and transfer control to another location within the code. If there were multiple conditional branches, they would need
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to be combined into a single decision point using logical operators, which would not increase the count of separate
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conditional branches.
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$$
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\therefore N \in \{0, 1\}
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$$
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where \( N \) is either \( 0 \) (no conditional branch) or \( 1 \) (one conditional branch).
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Similarly, a basic block can have at most one unconditional branch. An unconditional branch is typically used to exit
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the block and jump to another location in the code. If there were multiple unconditional branches, they would be
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redundant, as only one of them would be executed.
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$$
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\therefore M \in \{0, 1\}
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$$
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where \( M \) is either \( 0 \) (no unconditional branch) or \( 1 \) (one unconditional branch).
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If a basic block contains a conditional branch (\( N = 1 \)), it is not possible to have an unconditional branch (\( M =
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0 \)), as the control flow would be determined solely by the conditional branch. Conversely, if a basic block includes
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an unconditional branch (\( M = 1 \)), it is not feasible to have a conditional branch (\( N = 0 \)), as the
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unconditional branch would override any conditional decision.
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Logically -
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$$
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N \times M = 0
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$$
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$$
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(N = 1) \Rightarrow (M = 0)
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$$
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$$
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(M = 1) \Rightarrow (N = 0)
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$$
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That means that only one of \( N \) or \( M \) can have the value of \( 1 \) at any given time.
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If \( N \) is set to \( 1 \), \( M \) must be set to \( 0 \), and vice versa.
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We can use this relationship to check the functionality of our BB compiler pass and sanity of our training dataset.
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