Kamel Mekhnacha

16 papers A 1B 4C 2Journal 3Unranked 4
YearRankTypeTitle / Venue / Authors
2018 B conf
ESANN
Christine Sinoquet, Kamel Mekhnacha
2018 B conf
DSAA
Christine Sinoquet, Kamel Mekhnacha
2018 B conf
IDA
Christine Sinoquet, Kamel Mekhnacha
2017 B conf
RO-MAN
Alexis Mignon, Alban Bronisz, Ronan Le Hy, Kamel Mekhnacha, Luís Santos
2011 conf
DoCEIS
Hadi Aliakbarpour, Kamrad Khoshhal, João Quintas, Kamel Mekhnacha, Julien Ros, Maria Andersson, Jorge Dias
2011 conf
DoCEIS
Kamrad Khoshhal, Hadi Aliakbarpour, Kamel Mekhnacha, Julien Ros, João Quintas, Jorge Dias
2011 J jnl
IEEE Intell. Transp. Syst. Mag.
Christian Laugier, Igor E. Paromtchik, Mathias Perrollaz, Yong Mao, John-David Yoder, Christopher Tay, Kamel Mekhnacha, Amaury Nègre
2010 C conf
FUSION
Julien Ros, Kamel Mekhnacha
2009 conf
DPS
Julien Ros, Kamel Mekhnacha
2008 ch.
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems
Kamel Mekhnacha, Pierre Bessière
2008 conf
MFI
Kamel Mekhnacha, Yong Mao, David Raulo, Christian Laugier
2008 ch.
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems
M. K. Tay, Kamel Mekhnacha, Manuel Yguel, Christophe Coué, Cédric Pradalier, Christian Laugier, Thierry Fraichard, Pierre Bessière
2007 J jnl
Rev. d'Intelligence Artif.
Kamel Mekhnacha, Juan Manuel Ahuactzin, Pierre Bessière, Emmanuel Mazer, Linda Smail
2006 C conf
ICARCV
Cheng Chen, Christopher Tay, Christian Laugier, Kamel Mekhnacha
2001 J jnl
Adv. Robotics
Kamel Mekhnacha, Emmanuel Mazer, Pierre Bessière
2000 A conf
IROS
Kamel Mekhnacha, Emmanuel Mazer, Pierre Bessière
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)