Features extraction
Note
This tutorial is adapted from the demonstration presented in GreHack'22.
Context
Let's try to reproduce one of the feature extractor of the great How Machine Learning Is Solving the Binary Function
Similarity Problem1 paper.
Idea
This tutorial simply tries to demonstrate how to perform common feature extraction using Quokka API instead of using
IDA's API. By using Quokka, you gain several advantages:
- A saner API
- A compatibility with multiple IDA versions
- Reusable scripts
- A faster feature extraction
Original code
The original code for this tutorial is found in Cisco-Talos repository and reproduced below just for documentation purpose.
From
[https://github.com/Cisco-Talos/binary_function_similarity/blob/main/IDA_scripts/IDA_acfg_features/IDA_acfg_features.py](https://github.com/Cisco-Talos/binary_function_similarity/blob/main/IDA_scripts/IDA_acfg_features/IDA_acfg_features.py)
##############################################################################
# #
# Code for the USENIX Security '22 paper: #
# How Machine Learning Is Solving the Binary Function Similarity Problem. #
# #
# MIT License #
# #
# Copyright (c) 2019-2022 Cisco Talos #
# #
# Permission is hereby granted, free of charge, to any person obtaining #
# a copy of this software and associated documentation files (the #
# "Software"), to deal in the Software without restriction, including #
# without limitation the rights to use, copy, modify, merge, publish, #
# distribute, sublicense, and/or sell copies of the Software, and to #
# permit persons to whom the Software is furnished to do so, subject to #
# the following conditions: #
# #
# The above copyright notice and this permission notice shall be #
# included in all copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, #
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF #
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND #
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE #
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION #
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION #
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #
# #
# IDA_acfg_features.py - acfg_features IDA plugin implementation. #
# #
# This plugin contains code from: #
# github.com/williballenthin/python-idb/ licensed under Apache License 2.0 #
# #
##############################################################################
import idautils
import idc
import json
import ntpath
import os
import time
from capstone import *
from collections import namedtuple
from core import *
BasicBlock = namedtuple('BasicBlock', ['va', 'size', 'succs'])
def get_bitness():
"""Return 32/64 according to the binary bitness."""
info = idaapi.get_inf_structure()
if info.is_64bit():
return 64
elif info.is_32bit():
return 32
def initialize_capstone(procname, bitness):
"""
Initialize the Capstone disassembler.
Original code from Willi Ballenthin (Apache License 2.0):
https://github.com/williballenthin/python-idb/blob/
2de7df8356ee2d2a96a795343e59848c1b4cb45b/idb/idapython.py#L874
"""
md = None
arch = ""
# WARNING: mipsl mode not supported here
if procname == 'mipsb':
arch = "MIPS"
if bitness == 32:
md = Cs(CS_ARCH_MIPS, CS_MODE_MIPS32 | CS_MODE_BIG_ENDIAN)
if bitness == 64:
md = Cs(CS_ARCH_MIPS, CS_MODE_MIPS64 | CS_MODE_BIG_ENDIAN)
if procname == "arm":
arch = "ARM"
if bitness == 32:
# WARNING: THUMB mode not supported here
md = Cs(CS_ARCH_ARM, CS_MODE_ARM)
if bitness == 64:
md = Cs(CS_ARCH_ARM64, CS_MODE_ARM)
if "pc" in procname:
arch = "x86"
if bitness == 32:
md = Cs(CS_ARCH_X86, CS_MODE_32)
if bitness == 64:
md = Cs(CS_ARCH_X86, CS_MODE_64)
if md is None:
raise RuntimeError(
"Capstone initialization failure ({}, {})".format(
procname, bitness))
# Set detail to True to get the operand detailed info
md.detail = True
return md, arch
def capstone_disassembly(md, ea, size):
"""Disassemble a basic block using Capstone."""
try:
# Define a fixed constant to extract immediates
max_imm = 4096
bb_heads = list()
bb_mnems = list()
bb_disasm = list()
bb_numerics = list()
# Get the binary data corresponding to the instruction.
binary_data = idc.get_bytes(ea, size)
# Iterate over each instruction in the BB
for i_inst in md.disasm(binary_data, ea):
# Get the address
bb_heads.append(i_inst.address)
# Get the mnemonic
bb_mnems.append(i_inst.mnemonic)
# Get the disasm
bb_disasm.append("{} {}".format(
i_inst.mnemonic,
i_inst.op_str))
# Iterate over the operands
for op in i_inst.operands:
# Type immediate
if (op.type == 2):
if op.imm <= max_imm:
bb_numerics.append(op.imm)
return bb_heads, bb_mnems, bb_disasm, bb_numerics
except Exception as e:
print("[!] Capstone exception", e)
return list(), list(), list(), list()
def get_basic_blocks(fva):
"""Return the list of BasicBlock for a given function."""
bb_list = list()
func = idaapi.get_func(fva)
if func is None:
return bb_list
for bb in idaapi.FlowChart(func):
# WARNING: this function includes the BBs with size 0
# This is different from what IDA_acfg_disasm does.
# if bb.end_ea - bb.start_ea > 0:
bb_list.append(
BasicBlock(
va=bb.start_ea,
size=bb.end_ea - bb.start_ea,
succs=[x.start_ea for x in bb.succs()]))
return bb_list
def get_bb_disasm(bb, md):
"""Wrapper around a basic block disassembly."""
bb_bytes = idc.get_bytes(bb.va, bb.size)
bb_heads, bb_mnems, bb_disasm, bb_numerics = \
capstone_disassembly(md, bb.va, bb.size)
return bb_bytes, bb_heads, bb_mnems, bb_disasm, bb_numerics
def get_bb_features(bb, string_list, md, arch):
"""Extract the features associated to a BB."""
features_dict = dict()
# Corner case
if bb.size == 0:
features_dict = dict(
bb_len=0,
# BB list-type features
bb_numerics=list(),
bb_strings=list(),
# BB numerical-type features
n_numeric_consts=0,
n_string_consts=0,
n_instructions=0,
n_arith_instrs=0,
n_call_instrs=0,
n_logic_instrs=0,
n_transfer_instrs=0,
n_redirect_instrs=0
)
return features_dict
# Get the BB bytes, disassembly, mnemonics and other features
bb_bytes, bb_heads, bb_mnems, bb_disasm, bb_numerics = \
get_bb_disasm(bb, md)
# Get static strings from the BB
bb_strings = get_bb_strings(bb, string_list)
features_dict = dict(
bb_len=bb.size,
# BB list-type features
bb_numerics=bb_numerics,
bb_strings=bb_strings,
# BB numerical-type features
n_numeric_consts=len(bb_numerics),
n_string_consts=len(bb_strings),
n_instructions=len(bb_mnems),
n_arith_instrs=get_n_arith_instrs(bb_mnems, arch),
n_call_instrs=get_n_call_instrs(bb_mnems, arch),
n_logic_instrs=get_n_logic_instrs(bb_mnems, arch),
n_transfer_instrs=get_n_transfer_instrs(bb_mnems, arch),
n_redirect_instrs=get_n_redirect_instrs(bb_mnems, arch)
)
return features_dict
def run_acfg_features(idb_path, fva_list, output_dir):
"""Extract the features from each function. Save results to JSON."""
print("[D] Processing: %s" % idb_path)
# Create output directory if it does not exist
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
output_dict = dict()
output_dict[idb_path] = dict()
procname = idaapi.get_inf_structure().procName.lower()
bitness = get_bitness()
md, arch = initialize_capstone(procname, bitness)
# Get the list of Strings for the IDB
string_list = list(idautils.Strings())
# Iterate over each function
for fva in fva_list:
try:
start_time = time.time()
nodes_set, edges_set = set(), set()
bbs_dict = dict()
for bb in get_basic_blocks(fva):
# CFG
nodes_set.add(bb.va)
for dest_ea in bb.succs:
edges_set.add((bb.va, dest_ea))
# BB-level features
bbs_dict[bb.va] = get_bb_features(bb, string_list, md, arch)
# Function-level features
function_features = get_function_features(
fva, bbs_dict, len(edges_set))
elapsed_time = time.time() - start_time
func_dict = {
'nodes': list(nodes_set),
'edges': list(edges_set),
'features': function_features,
'basic_blocks': bbs_dict,
'elapsed_time': elapsed_time,
}
output_dict[idb_path][hex(fva)] = func_dict
except Exception as e:
print("[!] Exception: skipping function fva: %d" % fva)
print(e)
out_name = ntpath.basename(idb_path.replace(".i64", "_acfg_features.json"))
with open(os.path.join(output_dir, out_name), "w") as f_out:
json.dump(output_dict, f_out)
if __name__ == '__main__':
if not idaapi.get_plugin_options("acfg_features"):
print("[!] -Oacfg_features option is missing")
idc.Exit(1)
plugin_options = idaapi.get_plugin_options("acfg_features").split(":")
if len(plugin_options) != 3:
print("[!] -Oacfg_features:INPUT_JSON:IDB_PATH:OUTPUT_DIR is required")
idc.Exit(1)
input_json = plugin_options[0]
idb_path = plugin_options[1]
output_dir = plugin_options[2]
with open(input_json) as f_in:
selected_functions = json.load(f_in)
if idb_path not in selected_functions:
print("[!] Error! IDB path (%s) not in %s" % (idb_path, input_json))
idc.Exit(1)
fva_list = selected_functions[idb_path]
print("[D] Found %d addresses" % len(fva_list))
run_acfg_features(idb_path, fva_list, output_dir)
idc.Exit(0)
-
Andrea Marcelli, Mariano Graziano, Xabier Ugarte-Pedrero, Yanick Fratantonio, Mohamad Mansouri, Davide Balzarotti. How Machine Learning Is Solving the Binary Function Similarity Problem. USENIX Security '22. ↩