Weijun Cheng

25 papers B 1C 4Misc 1Journal 10Unranked 9
YearRankTypeTitle / Venue / Authors
2026 J jnl
Biomed. Signal Process. Control.
Kai Ying, Jing Zhang, Yifei Xuan, Weijun Cheng, Han Li, Ming Zhang, Bo Yuan, Liqiang Wang
2025 J jnl
IEEE Internet Things J.
Weijun Cheng, Wenjing Wei, Xiaoshi Liu, Xintao Huan
2024 J jnl
Entropy
Junjie Wang, Fude Li, Weijun Cheng
2024 J jnl
Expert Syst. Appl.
Kangjie Cao, Weijun Cheng, Yiya Hao, Yichao Gan, Ruihuan Gao, Junxu Zhu, Jinyao Wu
2024 conf
ICIC (3)
Kangjie Cao, Xiali Li, Jinyao Wu, Hu Yuan, Wentao Li, Jiayun Li, He Huang, Jueqiao Huang, Weijun Cheng
2024 conf
ICIC (12)
Yuelong Li, Wentao Wang, Weijun Cheng, Gaofeng Nie
2023 C conf
ICCC
Weijun Cheng, Junhao Chen, Xiaoshi Liu, Gaofeng Nie
2023 J jnl
IEEE Commun. Lett.
Weijun Cheng, Zhangfei Hu, Tengfei Ma, Gaofeng Nie
2023 conf
ICCC Workshops
Weijun Cheng, Xiaoshi Liu, Gaofeng Nie
2022 J jnl
IEEE Access
Weijun Cheng, Xiaoshi Liu, Xiaoting Wang, Gaofeng Nie
2021 C conf
ICCC
Xiaoting Wang, Weijun Cheng, Subing Zhang
2021 conf
ICCC Workshops
Xiaoting Wang, Weijun Cheng, Chenshan Ren
2021 J jnl
Sensors
Weijun Cheng, Xiaoting Wang, Tengfei Ma, Gang Wang
2020 J jnl
IEEE Access
Weijun Cheng, Xiaoting Wang
2020 conf
WCSP
Xiaoting Wang, Weijun Cheng, Xianmeng Xu
2019 J jnl
Sensors
Shaofei Sun, Hongxin Zhang, Liang Dong, Xiaotong Cui, Weijun Cheng, Muhammad Saad Khan
2019 Misc conf
TRIDENTCOM
Weijun Cheng, Xianmeng Xu, Xiaoting Wang, Xiaohan Liu
2015 conf
CCBR
Chuanlei Zhang, Shanwen Zhang, JuCheng Yang, Weijun Cheng
2014 conf
WCSP
Weijun Cheng
2013 J jnl
J. Comput.
Weijun Cheng
2013 conf
WOCC
Weijun Cheng
2013 conf
WCSP
Weijun Cheng
2009 C conf
HPCC
Hui Tian, Fan Jiang, Weijun Cheng
2006 B conf
GLOBECOM
Bocheng Zhu, Weijun Cheng
2004 C conf
ISCC
Weijun Cheng, Laibo Zheng, Jiandong Hu
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)