Maja Pivec

16 papers Journal 9Unranked 7
YearRankTypeTitle / Venue / Authors
2021 conf
SEEDA-CECNSM
Maja Pivec, Anika Kronberger
2018 conf
LaTiCE
Maja Pivec
2016 conf
VS-GAMES
Maja Pivec, Anika Kronberger
2012 J jnl
Artif. Intell. Medicine
Stephan Dreiseitl, Maja Pivec, Michael Binder
2011 J jnl
Int. J. Game Based Learn.
Paul Pivec, Maja Pivec
2007 J jnl
Br. J. Educ. Technol.
Maja Pivec
2007 J jnl
Informatica (Slovenia)
Maja Pivec, Paul Kearney
2007 J jnl
Br. J. Educ. Technol.
Paul Kearney, Maja Pivec
2006 J jnl
Informatica (Slovenia)
Maja Pivec, Christian Trummer, Jürgen Pripfl
2004 J jnl
Int. J. Intell. Games Simul.
Christos Bouras, Vaggelis Igglesis, Vaggelis Kapoulas, Ioannis Misedakis, Olga Dziabenko, Anni Koubek, Maja Pivec, A. Sfiri
2004 J jnl
J. Univers. Comput. Sci.
Maja Pivec, Olga Dziabenko
2004 J jnl
J. Univers. Comput. Sci.
Maja Pivec, K. Baumann
2003 conf
GAME-ON
Olga Dziabenko, Maja Pivec, Christos Bouras, Vaggelis Igglesis, Vaggelis Kapoulas, Ioannis Misedakis
1999 conf
WebNet
Thomas Dietinger, Christian Eller, Christian Gütl, Hermann A. Maurer, Maja Pivec
1999 conf
WebNet
Christian Gütl, Axel Jurak, Joseph Moser, Dietmar Neussl, Maja Pivec
1998 conf
WebNet
Thomas Dietinger, Christian Gütl, Hermann A. Maurer, Klaus Schmaranz, Maja Pivec
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)