Natasa Djurdjevac Conrad

15 papers Journal 14Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
Appl. Netw. Sci.
Natasa Djurdjevac Conrad, Elisa Tonello, Johannes Zonker, Heike Siebert
2024 J jnl
CoRR
Maia Trower, Natasa Djurdjevac Conrad, Stefan Klus
2024 J jnl
CoRR
Filip Blaskovic, Tim O. F. Conrad, Stefan Klus, Natasa Djurdjevac Conrad
2024 J jnl
J. Comput. Sci.
Jan-Hendrik Niemann, Samuel Uram, Sarah Wolf, Natasa Djurdjevac Conrad, Martin Weiser
2024 J jnl
CoRR
Sebastian Zimper, Federico Cornalba, Natasa Djurdjevac Conrad, Ana Djurdjevac
2023 J jnl
J. Nonlinear Sci.
Stefan Klus, Natasa Djurdjevac Conrad
2022 J jnl
CoRR
Natasa Djurdjevac Conrad, Jonas Köppl, Ana Djurdjevac
2022 J jnl
Entropy
Natasa Djurdjevac Conrad, Jonas Köppl, Ana Djurdjevac
2022 J jnl
Appl. Netw. Sci.
Margarita Kostré, Vikram Sunkara, Christof Schütte, Natasa Djurdjevac Conrad
2019 J jnl
BMC Bioinform.
Denise Thiel, Natasa Djurdjevac Conrad, Evgenia Ntini, Ria X. Peschutter, Heike Siebert, Annalisa Marsico
2018 J jnl
EPJ Data Sci.
Natasa Djurdjevac Conrad, Luzie Helfmann, Johannes Zonker, Stefanie Winkelmann, Christof Schütte
2017 conf
COMPLEX NETWORKS
Philipp Lorenz, Frederik Wolf, Jonas Braun, Natasa Djurdjevac Conrad, Philipp Hövel
2016 J jnl
Multiscale Model. Simul.
Natasa Djurdjevac Conrad, Marcus Weber, Christof Schütte
2014 J jnl
CoRR
Ralf Banisch, Christof Schütte, Natasa Djurdjevac Conrad
2014 J jnl
CoRR
Natasa Djurdjevac Conrad, Ralf Banisch, Christof Schütte
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)