Cham Athwal

16 papers A* 2Misc 1Journal 3Unranked 10
YearRankTypeTitle / Venue / Authors
2017 conf
ISMAR Adjunct
Mathew Randall, Ian Williams, Cham Athwal
2015 J jnl
IEEE Trans. Vis. Comput. Graph.
Gregory Hough, Ian Williams, Cham Athwal
2014 conf
ISMIR
Münevver Köküer, Peter Jancovic, Islah Ali-MacLachlan, Cham Athwal
2014 A* conf
ISMAR
Gregory Hough, Ian Williams, Cham Athwal
2014 A* conf
ISMAR
Gregory Hough, Ian Williams, Cham Athwal
2014 conf
DLfM@JCDL
Münevver Köküer, Daithí Kearney, Islah Ali-MacLachlan, Peter Jancovic, Cham Athwal
2013 conf
WASPAA
Dominic Ward, Cham Athwal, Münevver Köküer
2013 J jnl
Comput. Sci.
Andrew M. Thomas, Hanifa Shah, Philip Moore, Cain Evans, Mak Sharma, Sarah Mount, Hai V. Pham, Keith Osman, Anthony J. Wilcox, Peter Rayson, Craig Chapman, Parmjit Chima, Cham Athwal, David While
2013 conf
BTAS
Igor Barros Barbosa, Theoharis Theoharis, Christian Schellewald, Cham Athwal
2012 conf
ICHIT (1)
Greg Hough, Cham Athwal, Ian Williams
2012 conf
ICHIT (1)
Cham Athwal
2012 Misc conf
CISIS
Andrew M. Thomas, Hanifa Shah, Philip Moore, Peter Rayson, Anthony J. Wilcox, Keith Osman, Cain Evans, Craig Chapman, Cham Athwal, David While, Hai V. Pham, Sarah Mount
2011 conf
CMMR/FRSM
Ryan Stables, Cham Athwal, Jamie Bullock
2011 conf
ICMC
Ryan Stables, Cham Athwal, Jamie Bullock
2003 J jnl
Multim. Syst.
Cham Athwal, Jimmy Robinson
1997 conf
ECMAST
Alan Cole, Jimmy Robinson, Cham Athwal
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)