J. Leonel Rocha

22 papers Journal 17Unranked 5
YearRankTypeTitle / Venue / Authors
2026 J jnl
Commun. Nonlinear Sci. Numer. Simul.
J. Leonel Rocha, Abdel-Kaddous Taha, Daniele Fournier-Prunaret
2025 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha, Daniele Fournier-Prunaret
2023 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha
2023 J jnl
Commun. Nonlinear Sci. Numer. Simul.
J. Leonel Rocha, Abdel-Kaddous Taha
2021 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha
2021 J jnl
Math. Comput. Simul.
J. Leonel Rocha, Sonia Carvalho
2020 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha
2019 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha
2017 J jnl
Int. J. Bifurc. Chaos
Luís Silva, J. Leonel Rocha, Teresa Morais Silva
2017 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha, Daniele Fournier-Prunaret
2016 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha, Daniele Fournier-Prunaret
2016 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, Abdel-Kaddous Taha, Daniele Fournier-Prunaret
2013 J jnl
Int. J. Bifurc. Chaos
Daniele Fournier-Prunaret, J. Leonel Rocha, Acilina Caneco, Sara Fernandes, Clara Grácio
2012 J jnl
J. Comput. Inf. Technol.
Sandra M. Aleixo, J. Leonel Rocha
2012 conf
ITI
Sandra M. Aleixo, J. Leonel Rocha
2012 conf
ITI
J. Leonel Rocha, Sandra M. Aleixo
2009 conf
ITI
Sandra M. Aleixo, J. Leonel Rocha, Dinis D. Pestana
2009 conf
ITI
Dinis D. Pestana, Sandra M. Aleixo, J. Leonel Rocha
2009 J jnl
Int. J. Bifurc. Chaos
Acilina Caneco, J. Leonel Rocha, Clara Grácio
2008 conf
ITI
Sandra M. Aleixo, J. Leonel Rocha, Dinis D. Pestana
2004 J jnl
Int. J. Math. Math. Sci.
J. Leonel Rocha, J. Sousa Ramos
2003 J jnl
Int. J. Bifurc. Chaos
J. Leonel Rocha, J. Sousa Ramos
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)