Vasilios Zorkadis

26 papers A 1C 1Journal 8Unranked 12
YearRankTypeTitle / Venue / Authors
2021 conf
PCI
Rozita Tsoni, Vasilios Zorkadis, Vassilios S. Verykios
2020 ed.
e-Democracy
Sokratis K. Katsikas, Vasilios Zorkadis
2017 ed.
e-Democracy
Sokratis K. Katsikas, Vasilios Zorkadis
2017 J jnl
Comput. Law Secur. Rev.
Apostolos Malatras, Ignacio Sánchez, Laurent Beslay, Iwen Coisel, Ioannis Vakalis, Giuseppe D'Acquisto, Manuel García Sánchez, Matthieu Grall, Marit Hansen, Vasilios Zorkadis
2014 ed.
e-Democracy
Alexander B. Sideridis, Zoe Kardasiadou, Constantine P. Yialouris, Vasilios Zorkadis
2011 conf
ICGS3/e-Democracy
Stergios G. Tsiafoulis, Vasilios Zorkadis, Elias Pimenidis
2010 C conf
CIS
Stergios G. Tsiafoulis, Vasilios Zorkadis
2010 conf
FGIT-GDC/CA
Stergios G. Tsiafoulis, Vasilios Zorkadis, Dimitris A. Karras
2010 conf
Panhellenic Conference on Informatics
Ioannis Panagiotopoulos, Lambrini Seremeti, Achilles Kameas, Vasilios Zorkadis
2009 J jnl
Int. J. Electron. Secur. Digit. Forensics
Vasilios Zorkadis, Dimitrios A. Karras
2008 conf
NTMS
Dimitris A. Karras, Vasilios Zorkadis
2008 conf
ICONIP (1)
Dimitrios A. Karras, Vasilios Zorkadis
2007 conf
IICAI
Dimitris A. Karras, Vasilios Zorkadis
2007 J jnl
Int. J. Electron. Secur. Digit. Forensics
Vasilios Zorkadis
2006 J jnl
J. Exp. Theor. Artif. Intell.
Vasilios Zorkadis, Dimitris A. Karras
2005 conf
IICAI
Vasilios Zorkadis, Dimitris A. Karras, M. Panayotou
2005 J jnl
Neural Networks
Vasilios Zorkadis, Dimitris A. Karras, M. Panayotou
2004 J jnl
Inf. Manag. Comput. Secur.
Vasilios Zorkadis, P. Donos
2003 J jnl
Neural Networks
Dimitris A. Karras, Vasilios Zorkadis
2003 conf
IICAI
Dimitris A. Karras, Vasilios Zorkadis
2002 conf
InfraSec
E. S. Siougle, Vasilios Zorkadis
2002 conf
Australian Joint Conference on Artificial Intelligence
Dimitris A. Karras, Vasilios Zorkadis
2000 conf
EUROMICRO
Dimitris A. Karras, Vasilios Zorkadis
1998 J jnl
Neural Parallel Sci. Comput.
Dimitris A. Karras, Vasilios Zorkadis
1995
Vasilios Zorkadis
1994 A conf
ESORICS
Vasilios Zorkadis
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)