Ioannis Tsougos

11 papers C 1Journal 9Unranked 1
YearRankTypeTitle / Venue / Authors
2025 C conf
BIBE
Dimitrios Samaras, Georgios Agrotis, Maria Vakalopoulou, A. Vassiou, Marianna Vlychou, Ioannis Tsougos
2025 J jnl
J. Imaging
George Angelidis, Stavroula Giannakou, Varvara Valotassiou, Emmanouil Panagiotidis, Ioannis Tsougos, Chara Tzavara, Dimitrios Psimadas, Evdoxia Theodorou, Charalampos Ziangas, John Skoularigis, Filippos Triposkiadis, Panagiotis Georgoulias
2024 conf
ISBI
Sotiris Raptis, Ioannis Tsougos, Kiki Theodorou, Christos Ilioudis
2022 J jnl
Appl. Clin. Inform.
Ioannis Tamposis, Ioannis Tsougos, Anastasios Karatzas, Katerina Vassiou, Marianna Vlychou, Vasileios Tzortzis
2019 J jnl
IEEE Trans. Medical Imaging
Lampros Theodorakis, George Loudos, V. Prassopoulos, Constantine Kappas, Ioannis Tsougos, Panagiotis Georgoulias
2018 J jnl
Comput. Math. Methods Medicine
Ioannis Tsougos, Alexandros Vamvakas, Constantine Kappas, Ioannis Fezoulidis, Katerina Vassiou
2018 J jnl
Biomed. Signal Process. Control.
Alexandros Vamvakas, Ioannis Tsougos, Nikolaos Arikidis, Eftychia E. Kapsalaki, Konstantinos Fountas, Ioannis Fezoulidis, Lena Costaridou
2015 J jnl
Int. J. Comput. Assist. Radiol. Surg.
Evangelia E. Tsolaki, Patricia Svolos, Evanthia Kousi, Eftychia E. Kapsalaki, Ioannis Fezoulidis, Konstantinos Fountas, Kyriaki Theodorou, Constantine Kappas, Ioannis Tsougos
2013 J jnl
Int. J. Comput. Assist. Radiol. Surg.
Evangelia E. Tsolaki, Patricia Svolos, Evanthia Kousi, Eftychia E. Kapsalaki, Konstantinos Fountas, Kyriaki Theodorou, Ioannis Tsougos
2011 J jnl
Biomed. Signal Process. Control.
Ioannis N. Dimou, Ioannis Tsougos, Evaggelia E. Tsolaki, Evanthia Kousi, Eftychia E. Kapsalaki, Kyriaki Theodorou, Michail G. Kounelakis, Michalis E. Zervakis
2011 J jnl
IEEE Trans. Inf. Technol. Biomed.
Michail G. Kounelakis, Ioannis N. Dimou, Michael E. Zervakis, Ioannis Tsougos, Evangelia E. Tsolaki, Evanthia Kousi, Eftychia E. Kapsalaki, Kyriaki Theodorou
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)