Hajime Morita

19 papers A* 1B 1C 1Misc 3Journal 2Unranked 11
YearRankTypeTitle / Venue / Authors
2023 B conf
SMC
Kazuya Ohata, Hitoshi Iyatomi, Hajime Morita, Kojiro Iizuka
2023 conf
ECIR (1)
Kojiro Iizuka, Hajime Morita, Makoto P. Kato
2023 J jnl
CoRR
Kojiro Iizuka, Hajime Morita, Makoto P. Kato
2021 Misc conf
RANLP
An Nguyen Le, Hajime Morita, Tomoya Iwakura
2019 Misc conf
RANLP
Hajime Morita, Tomoya Iwakura
2017 conf
IWPT
Daisuke Kawahara, Yuta Hayashibe, Hajime Morita, Sadao Kurohashi
2016 conf
NTCIR
Makoto P. Kato, Tetsuya Sakai, Takehiro Yamamoto, Virgil Pavlu, Hajime Morita, Sumio Fujita
2016 conf
EVIA@NTCIR
Makoto P. Kato, Virgil Pavlu, Tetsuya Sakai, Takehiro Yamamoto, Hajime Morita
2015 conf
EVENTS@HLP-NAACL
Yu Takabatake, Hajime Morita, Daisuke Kawahara, Sadao Kurohashi, Ryuichiro Higashinaka, Yoshihiro Matsuo
2015 A* conf
EMNLP
Hajime Morita, Daisuke Kawahara, Sadao Kurohashi
2015 C conf
PACLIC
Kanako Komiya, Yuto Sasaki, Hajime Morita, Minoru Sasaki, Hiroyuki Shinnou, Yoshiyuki Kotani
2014 conf
JCKBSE
Maiko Yamazaki, Hajime Morita, Kanako Komiya, Yoshiyuki Kotani
2013 conf
ACL (1)
Hajime Morita, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
2013 conf
NTCIR
Hajime Morita, Ryohei Sasano, Hiroya Takamura, Manabu Okumura
2012 J jnl
Inf. Media Technol.
Hajime Morita, Tetsuya Sakai, Manabu Okumura
2012 conf
JCKBSE
Yuji Abe, Hajime Morita, Kanako Komiya, Yoshiyuki Kotani
2011 conf
ACL (2)
Hajime Morita, Tetsuya Sakai, Manabu Okumura
2011 conf
NTCIR
Hajime Morita, Takuya Makino, Tetsuya Sakai, Hiroya Takamura, Manabu Okumura
2009 Misc conf
RANLP
Hajime Morita, Hiroya Takamura, Manabu Okumura
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)