Olena Havrylenko

11 papers Misc 5Unranked 6
YearRankTypeTitle / Venue / Authors
2025 conf
ProfIT AI
Olena Yashyna, Tetiana Boiarska, Eduard Khomiak, Olena Havrylenko, Pavlo Hrynevych
2023 conf
ICCSA (1)
Anatoliy Popov, Eduard Tserne, Valerii Volosyuk, Simeon Zhyla, Vladimir Pavlikov, Nikolay Ruzhentsev, Kostiantyn Dergachov, Olena Havrylenko, Oleksandr Shmatko, Yuliya Averyanova, Ivan Ostroumov, Nataliia Kuzmenko, Olga Sushchenko, Maksym Zaliskyi, Oleksandr Solomentsev, Borys Kuznetsov, Tatyana Nikitina
2022 conf
ICCSA (1)
Olha Sushchenko, Yuliya Averyanova, Ivan Ostroumov, Nataliia Kuzmenko, Maksym Zaliskyi, Oleksandr Solomentsev, Borys Kuznetsov, Tatyana Nikitina, Olena Havrylenko, Anatoliy Popov, Valerii Volosyuk, Oleksandr Shmatko, Nikolay Ruzhentsev, Simeon Zhyla, Vladimir Pavlikov, Kostiantyn Dergachov, Eduard Tserne
2022 conf
ProfIT AI
Olena Havrylenko, Anatoly Kulik, Andriy Chukhray
2021 conf
ICCSA (2)
Ivan Ostroumov, Nataliia Kuzmenko, Olga Sushchenko, Maksym Zaliskyi, Oleksandr Solomentsev, Yuliya Averyanova, Simeon Zhyla, Vladimir Pavlikov, Eduard Tserne, Valerii Volosyuk, Kostiantyn Dergachov, Olena Havrylenko, Oleksandr Shmatko, Anatoliy Popov, Nikolay Ruzhentsev, Borys Kuznetsov, Tatyana Nikitina
2020 Misc conf
ICTERI
Valeriy Mygal, Galyna Mygal, Andriy Chukhray, Olena Havrylenko
2020 Misc conf
ICTERI
Andrey Chukhray, Olena Havrylenko
2020 Misc conf
ICTERI
Andrey Chukhray, Olena Havrylenko, Valeriy Mygal, Galyna Mygal
2019 Misc conf
ICTERI
Ievgen Vagin, Olena Havrylenko, Juan Pablo Martínez Bastida, Andrey Chukhray
2019 Misc conf
ICTERI
Daniel Gaydachuk, Olena Havrylenko, Juan Pablo Martínez Bastida, Andrey Chukhray
2017 conf
ICTERI (Revised Selected Papers)
Juan Pablo Martínez Bastida, Olena Havrylenko, Andrey Chukhray
redb/extractors/decompiler/bninja/similarity/minhasher.py
← Index redb/extractors/decompiler/bninja/similarity/minhasher.py python
import logging
import random
from enum import Enum

from ..analysis.medium_level_normalization import MediumLevelNormalization

try:
    from .minhashcustom import MinHashCustom
    from ..analysis.low_level_normalization import LowLevelNormalization
except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.similarity.minhashcustom import MinHashCustom
    from redb.extractors.decompiler.bninja.analysis.low_level_normalization import LowLevelNormalization

## Values for this configuration were extracted from https://github.com/danielplohmann/mcrit/blob/main/mcrit/config/MinHashConfig.py#L10
# Length in number of Shingles of which a minhash consists
# this value represents the length of sha256sum hash truncated
MINHASH_SIGNATURE_LENGTH: int = 64
# Number of bits per signature element (1-32 bits)
MINHASH_SIGNATURE_BITS: int = 8


class TokenKind(Enum):
    LLIL = "llil"
    TYPED_LLIL = "typed_llil"
    MLIL = "mlil"
    TYPED_MLIL = "typed_mlil"


class MinHasher:
    # stick to the default method
    MINHASH_STRATEGY_HASH_ALL = 1

    def __init__(self, seed, il_function, kind: TokenKind = TokenKind.LLIL):
        self._minhash_seeds = []
        self.il_func = il_function
        self.kind = kind
        self._minhash_permutation = []
        self._signature_segments = []
        self._initMinhashing(seed)

    def _initMinhashing(self, MINHASH_SEED=None):
        random.seed(MINHASH_SEED)
        # init sequence of seeds
        self._minhash_seeds = [
            random.randint(0, MinHashCustom.getHashMax()) for _ in range(MINHASH_SIGNATURE_LENGTH)
        ]

    def make_ngrams(self, tokens, n=3):
        """Take the ngrams of the IL we try to pass into the functions"""
        return [tuple(tokens[i:i+n]) for i in range(len(tokens) - n + 1)]

    def _extract_tokens(self):
        """Extract the IL tokens from the IL function, picking the right
        normalizer (LLIL/MLIL) and the right normalization mode
        (skeleton/typed) based on self.kind."""
        if self.kind in (TokenKind.LLIL, TokenKind.TYPED_LLIL):
            normalizer = LowLevelNormalization()
        elif self.kind in (TokenKind.MLIL, TokenKind.TYPED_MLIL):
            normalizer = MediumLevelNormalization()
        else:
            raise ValueError(f"Unsupported token kind: {self.kind}")

        # typed variants include operand type info, skeleton variants don't
        if self.kind in (TokenKind.TYPED_LLIL, TokenKind.TYPED_MLIL):
            normalize = normalizer.normalize_instr_with_operands
        else:
            normalize = normalizer.normalize_instruction_all_levels

        instructions = []
        for basic_block in self.il_func.basic_blocks:
            for il in basic_block:
                instructions.append(normalize(il))

        return instructions

    def calculateMinHash(self):
        """Calculate hash function every time, then take minimum shingle per shingler"""
        minhash_result = MinHashCustom(minhash_bits=MINHASH_SIGNATURE_BITS)
        minhash_signature = []

        tokens = self._extract_tokens()
        shingles = self.make_ngrams(tokens, n=3)

        # Functions with fewer than 3 IL instructions can't produce n-grams
        # Return empty minhash for such small functions (thunks, stubs, etc.)
        # Triggered by 39d8ad95b0323c37bd3134ab93ac4af44c66a1a8443a41c1ac02cec19bb2816a
        if not shingles:
            return []

        # Generate the MinHash
        for seed in self._minhash_seeds:
            hashed_shingles = [
                self.shingle_hash(shingle, seed) for shingle in shingles
            ]
            min_value = min(hashed_shingles)

            if MINHASH_SIGNATURE_BITS < 32:
                min_value %= (2 ** MINHASH_SIGNATURE_BITS)

            minhash_signature.append(min_value)

        minhash_result.setMinHash(minhash_signature)
        return minhash_result.getMinHashInt()

    def shingle_hash(self, shingle, hash_seed=0):
        """Produce a single 32bit UINT hash for a given shingle"""
        return MinHashCustom.hashData(shingle, hash_seed)