Chang Wang

36 papers Journal 36
YearRankTypeTitle / Venue / Authors
2026 J jnl
Comput. Ind. Eng.
Qinyu Sun, Hang Zhou, Rui Fu, Chang Wang, Yingshi Guo, Yueru Lang, Wei Yuan
2025 J jnl
IEEE Internet Things J.
Siyang Jiang, Menglu Gu, Yanqi Su, Chang Wang, Wenhui Wei
2025 J jnl
IEEE Trans. Veh. Technol.
Zhao Li, Xia Zhao, Chen Zhao, Yongtao Liu, Chang Wang
2025 J jnl
IEEE Trans. Intell. Transp. Syst.
Xia Zhao, Zhao Li, Chang Wang, Xiaoqiang Sun, Weiqi Zhou, Hongjia Zhang, Zhi Zhang
2025 J jnl
Eng. Appl. Artif. Intell.
Qinyu Sun, Hang Zhou, Rui Fu, Yaning Xu, Chang Wang, Yingshi Guo
2025 J jnl
IEEE Trans. Intell. Transp. Syst.
Yonghang Li, Chang Wang, Yifei Wang, Miao Ren, Jin Niu, Jikang Zhao, Kai Du
2024 J jnl
IEEE Trans. Intell. Transp. Syst.
Zhao Li, Xia Zhao, Fuwei Wu, Dan Chen, Chang Wang
2024 J jnl
Expert Syst. Appl.
Xia Zhao, Zhao Li, Chen Zhao, Rui Fu, Chang Wang
2024 J jnl
IEEE Trans. Intell. Transp. Syst.
Hailun Zhang, Rui Fu, Chang Wang, Yingshi Guo, Wei Yuan
2024 J jnl
Cogn. Technol. Work.
Rui Fu, Li Ma, Yingshi Guo, Qinyu Sun, Chang Wang, Wei Yuan, Tingting Lan
2024 J jnl
IEEE Trans. Veh. Technol.
Menglu Gu, Yanqi Su, Chang Wang, Yingshi Guo
2024 J jnl
IEEE Trans. Hum. Mach. Syst.
Xia Zhao, Zhao Li, Rui Fu, Chang Wang, Yingshi Guo
2023 J jnl
IEEE Trans. Intell. Transp. Syst.
Qinyu Sun, Yingshi Guo, Yongtao Liu, Chang Wang, Menglu Gu, Yanqi Su
2023 J jnl
Displays
Xia Zhao, Zhao Li, Chen Zhao, Chang Wang, Rui Fu
2022 J jnl
IEEE Internet Things J.
Hailun Zhang, Rui Fu, Chang Wang, Yingshi Guo, Wei Yuan
2021 J jnl
Expert Syst. Appl.
Qinyu Sun, Chang Wang, Rui Fu, Yingshi Guo, Wei Yuan, Zhen Li
2020 J jnl
IEEE Access
Chen Zhao, Siyang Jiang, Yi Lei, Chang Wang
2020 J jnl
EURASIP J. Wirel. Commun. Netw.
Hui Wang, Menglu Gu, Shengbo Wu, Chang Wang
2020 J jnl
J. Intell. Fuzzy Syst.
Chang Wang, Qinyu Sun, Zhen Li, Hongjia Zhang, Rui Fu
2020 J jnl
IEEE Access
Hui Wang, Hongjia Zhang, Menglu Gu, Chang Wang
2020 J jnl
IEEE Access
Mingyang Deng, Yingshi Guo, Rui Fu, Chang Wang
2020 J jnl
Sensors
Chang Wang, Qinyu Sun, Zhen Li, Hongjia Zhang
2020 J jnl
Sensors
Qinyu Sun, Yingshi Guo, Rui Fu, Chang Wang, Wei Yuan
2020 J jnl
IEEE Trans. Intell. Transp. Syst.
Chang Wang, Qinyu Sun, Yingshi Guo, Rui Fu, Wei Yuan
2020 J jnl
Sensors
Yang Zhou, Rui Fu, Chang Wang, Ruibin Zhang
2020 J jnl
Sensors
Qinyu Sun, Chang Wang, Yingshi Guo, Wei Yuan, Rui Fu
2020 J jnl
Sensors
Hongjia Zhang, Yanjuan Liu, Chang Wang, Rui Fu, Qinyu Sun, Zhen Li
2020 J jnl
Sensors
Rui Fu, Yali Zhang, Chang Wang, Wei Yuan, Yingshi Guo, Yong Ma
2019 J jnl
IEEE Trans. Veh. Technol.
Mingfang Zhang, Rui Fu, Daniel D. Morris, Chang Wang
2019 J jnl
IEEE Access
Qinyu Sun, Hongjia Zhang, Zhen Li, Chang Wang, Kang Du
2019 J jnl
Clust. Comput.
Rui Fu, Mingfang Zhang, Chang Wang
2019 J jnl
IEEE Access
Chang Wang, Qinyu Sun, Zhen Li, Hongjia Zhang, Kaili Ruan
2019 J jnl
IEEE Access
Yali Zhang, Wei Yuan, Rui Fu, Chang Wang
2019 J jnl
IEEE Access
Yingshi Guo, Qinyu Sun, Rui Fu, Chang Wang
2019 J jnl
IEEE Access
Zhi Zhang, Yingshi Guo, Wei Yuan, Chang Wang
2018 J jnl
Wirel. Pers. Commun.
Chang Wang, Yali Zhang, Menglu Gu
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)