Catherine L. Dumas

18 papers Journal 3Unranked 15
YearRankTypeTitle / Venue / Authors
2022 conf
DG.O
Catherine L. Dumas
2022 J jnl
Gov. Inf. Q.
Teresa M. Harrison, Catherine L. Dumas, Nic DePaula, Tim Fake, William May, Akanksha Atrey, Jooyeon Lee, Lokesh Rishi, S. S. Ravi
2022 conf
ASIST
Catherine L. Dumas, Rachel D. Williams, Joanna Flanagan, Lukasz Porwol
2021 J jnl
Inf. Polity
Catherine L. Dumas
2021 conf
DG.O
Lukasz Porwol, Catherine L. Dumas
2021 conf
ASIST
Loni Hagen, Devon L. Greyson, Ashley Fox, Kolina Koltai, Catherine L. Dumas
2021 conf
DG.O
Lukasz Porwol, Catherine L. Dumas
2020 conf
DG.O
Catherine L. Dumas
2019 conf
ASIST
Adam Mazel, Catherine L. Dumas
2019 conf
ASIST
Danielle Pollock, Rachel D. Williams, Catherine L. Dumas, Naresh Kumar Agarwal, Sanda Erdelez
2019 conf
ASIST
Catherine L. Dumas, Sanda Erdelez, Jeffrey Pomerantz, Vik Parthiban
2018 conf
ASIST
Juan Pablo Alperin, Catherine L. Dumas, Amir Karami, David Moscrop, Vivek Singh, Hassan Zamir, Aylin Ilhan, Isabelle Dorsch
2017 conf
DG.O
Teresa M. Harrison, Catherine L. Dumas, Nic DePaula, Tim Fake, William May, Akanksha Atrey, Jooyeon Lee, Lokesh Rishi, S. S. Ravi
2016 conf
DG.O
Catherine L. Dumas, Akanksha Atrey, Jooyeon Lee, Teresa M. Harrison, Tim Fake, Xiaoyi Zhao, S. S. Ravi
2015 conf
ASIST
Loni Hagen, Jessica Kropczynski, Catherine L. Dumas, Jisue Lee, Fatima K. Espinoza Vasquez, Abebe Rorissa
2015 J jnl
Big Data Soc.
Catherine L. Dumas, Dan Lamanna, Teresa M. Harrison, S. S. Ravi, Christopher Kotfila, Norman Gervais, Loni Hagen, Feng Chen
2015 conf
DG.O
Catherine L. Dumas
2011 conf
ASIST
Xiaojun Yuan, Nicholas J. Belkin, Chris Jordan, Catherine L. Dumas
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)