Olga N. Masina

13 papers Journal 4Unranked 9
YearRankTypeTitle / Venue / Authors
2025 J jnl
Program. Comput. Softw.
Alexey A. Petrov, Olga V. Druzhinina, Olga N. Masina, Anastasia V. Demidova
2024 J jnl
Program. Comput. Softw.
Anastasiya V. Demidova, Olga V. Druzhinina, Olga N. Masina, Alexey A. Petrov
2024 conf
OPTIMA
Alexey A. Petrov, Olga V. Druzhinina, Olga N. Masina
2024 J jnl
Autom. Remote. Control.
Olga V. Druzhinina, Alexey A. Petrov, Olga N. Masina
2023 J jnl
Program. Comput. Softw.
Anastasiya V. Demidova, Olga V. Druzhinina, Olga N. Masina, Alexey A. Petrov
2021 conf
ITTMM
Anastasia V. Demidova, Olga V. Druzhinina, Olga N. Masina, Alexander V. Shcherbakov
2021 conf
ITTMM
Olga V. Druzhinina, Olga N. Masina, Elena V. Igonina, Alexey A. Petrov
2020 conf
ITTMM
Anastasia V. Demidova, Olga V. Druzhinina, Olga N. Masina, Alexey A. Petrov
2020 conf
CSOC (2)
Olga V. Druzhinina, Olga N. Masina, Alexey A. Petrov, Evgeny V. Lisovsky, Maria A. Lyudagovskaya
2020 conf
OPTIMA
Anastasiya V. Demidova, Olga V. Druzhinina, Milojica Jacimovic, Olga N. Masina, Nevena Mijajlovic, Nicholas N. Olenev, Alexey A. Petrov
2019 conf
ITTMM (Selected Papers)
Anastasia V. Demidova, Olga V. Druzhinina, Olga N. Masina, Ekaterina D. Tarova
2018 conf
ICUMT
Olga V. Druzhinina, Elena A. Kaledina, Olga N. Masina, Vladimir N. Shchennikov, Elena V. Shchennikova
2018 conf
ICUMT
Anastasiya V. Demidova, Olga V. Druzhinina, Milojica Jacimovic, Olga N. Masina, Nevena Mijajlovic
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