Olivier Orfila

12 papers B 4Journal 4Unranked 4
YearRankTypeTitle / Venue / Authors
2023 J jnl
Sensors
Ting Wang, Meiting Tu, Hao Lyu, Ye Li, Olivier Orfila, Guojian Zou, Dominique Gruyer
2021 conf
ITSC
Jérémy Leroy, Dominique Gruyer, Olivier Orfila, Nour-Eddin El Faouzi
2021 J jnl
Sensors
Yacine Mohamed Idir, Olivier Orfila, Vincent Judalet, Benoît Sagot, Patrice Chatellier
2020 J jnl
Int. J. Data Sci. Anal.
Yann Méneroux, Arnaud Le Guilcher, Guillaume Saint-Pierre, Mohammad Ghasemi Hamed, Sébastien Mustière, Olivier Orfila
2019 conf
ITSC
Meiting Tu, Ye Li, Wenxiang Li, Minchao Tu, Olivier Orfila, Dominique Gruyer, XuegangJeff Ban
2018 conf
PerCom Workshops
Stavros Nousias, Christos Tselios, Dimitris Bitzas, Olivier Orfila, Samantha L. Jamson, Pablo Mejuto, Dimitrios Amaxilatis, Orestis Akrivopoulos, Ioannis Chatzigiannakis, Aris S. Lalos, Konstantinos Moustakas
2017 J jnl
Annu. Rev. Control.
Dominique Gruyer, Valentin Magnier, Karima Hamdi, Laurene Claussmann, Olivier Orfila, Andry Rakotonirainy
2015 B conf
Intelligent Vehicles Symposium
Olivier Orfila, Dominique Gruyer, Vincent Judalet, Marc Revilloud
2013 B conf
Intelligent Vehicles Symposium
Qi Cheng, Lydie Nouvelière, Olivier Orfila
2013 B conf
Intelligent Vehicles Symposium
Dominique Gruyer, Olivier Orfila, Vincent Judalet, Steve Pechberti, Benoit Lusetti, Sebastien Glaser
2013 B conf
Intelligent Vehicles Symposium
Sebastien Glaser, Olivier Orfila, Lydie Nouvelière, Roman Potarusov, Sagar Akhegaonkar, Frédéric Holzmann, Volker Scheuch
2013 conf
MIM
Roman Potarusov, Lydie Nouvelière, Olivier Orfila, Sebastien Glaser
redb/extractors/decompiler/bninja/analysis/medium_level.py
← Index redb/extractors/decompiler/bninja/analysis/medium_level.py python
import time

from binaryninja import (
    MediumLevelILOperation as MLIL_OP,
)

try:
    from ..function_type import FunctionTypeAnalysis
    from ..similarity.minhasher import MinHasher, TokenKind
    from ..utils.hashes import calculate_sha256, calculate_tlsh
    from .medium_level_normalization import MediumLevelNormalization
except ImportError:
    from redb.extractors.decompiler.bninja.analysis.medium_level_normalization import MediumLevelNormalization
    from redb.extractors.decompiler.bninja.similarity.minhasher import MinHasher
    from redb.extractors.decompiler.bninja.function_type import FunctionTypeAnalysis
    from redb.extractors.decompiler.bninja.utils.hashes import calculate_sha256, calculate_tlsh


_MLIL_CALL_OPS = (
    MLIL_OP.MLIL_CALL,
    MLIL_OP.MLIL_CALL_SSA,
    MLIL_OP.MLIL_CALL_UNTYPED,
    MLIL_OP.MLIL_CALL_UNTYPED_SSA,
    MLIL_OP.MLIL_TAILCALL,
    MLIL_OP.MLIL_TAILCALL_SSA,
    MLIL_OP.MLIL_TAILCALL_UNTYPED,
    MLIL_OP.MLIL_TAILCALL_UNTYPED_SSA,
)

_MLIL_CONTROL_FLOW_OPS = (
    MLIL_OP.MLIL_IF,
    MLIL_OP.MLIL_GOTO,
    MLIL_OP.MLIL_JUMP,
    MLIL_OP.MLIL_JUMP_TO,
    MLIL_OP.MLIL_RET,
    MLIL_OP.MLIL_RET_HINT,
    MLIL_OP.MLIL_NORET,
) + _MLIL_CALL_OPS


class MediumLevelAnalysis:
    def __init__(self, function, bv, logger):
        self.function = function
        self.name = function.name
        self.start = function.start
        self.mlil_func = function.mlil
        self.bv = bv
        self.logger = logger
        self.errors = []

    def log_error(self, message, function_name, address, exception=None, error_location="unknown"):
        error_msg = f"Error in function {function_name} at {address}: {message}"
        if exception:
            error_msg += f" - {str(exception)}"
        self.logger.error(error_msg)

        error = {
            "function_name": function_name,
            "function_address": str(address),
            "error_location": error_location,
            "error_message": message,
            "error_details": str(exception) if exception else "",
            "error_type": type(exception).__name__ if exception else "Unknown",
            "timestamp": int(time.time() * 1000),
        }
        self.errors.append(error)

    def _collect_mlil_skeleton_and_typed(self):
        mlil = self.mlil_func
        if not mlil:
            return [], [], [], []

        start = self.start
        norm = MediumLevelNormalization()

        skeleton = []
        skeleton_with_addr = []
        typed = []
        typed_with_addr = []

        for il in mlil.instructions:
            skel_norm = norm.normalize_instruction_all_levels(il)
            typed_norm = norm.normalize_instr_with_operands(il)

            skeleton.append(skel_norm)
            typed.append(typed_norm)

            offset = il.address - start
            if offset < 0:
                offset = 0

            skeleton_with_addr.append((offset, skel_norm))
            typed_with_addr.append((offset, typed_norm))

        return skeleton, skeleton_with_addr, typed, typed_with_addr

    def analyze(self):
        (
            instr_skeleton,
            body_mlil_skeleton_vector,
            instr_typed,
            body_mlil_typed_vector,
        ) = self._collect_mlil_skeleton_and_typed()

        instr_skeleton_str = str(instr_skeleton)
        sha256_skeleton = calculate_sha256(instr_skeleton_str)
        tlsh_skeleton = calculate_tlsh(instr_skeleton_str)

        instr_typed_str = str(instr_typed)
        sha256_typed = calculate_sha256(instr_typed_str)
        tlsh_typed = calculate_tlsh(instr_typed_str)

        seed = 0xdeadbeef
        minhash_mlil_skeleton = MinHasher(seed, self.mlil_func, TokenKind.MLIL).calculateMinHash()
        minhash_mlil_typed = MinHasher(seed, self.mlil_func, TokenKind.TYPED_MLIL).calculateMinHash()

        medium_level_json = {
            "function_address": self.start,
            "body_mlil_skeleton_vector": body_mlil_skeleton_vector,
            "sha256_mlil_skeleton": sha256_skeleton,
            "tlsh_mlil_skeleton": tlsh_skeleton,
            "minhash_mlil_skeleton": minhash_mlil_skeleton,
            "body_mlil_typed_vector": body_mlil_typed_vector,
            "sha256_mlil_typed": sha256_typed,
            "tlsh_mlil_typed": tlsh_typed,
            "minhash_mlil_typed": minhash_mlil_typed,
        }

        return medium_level_json, self.errors