Kannan Ramkumar

15 papers Journal 14Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
Int. J. Imaging Syst. Technol.
M. Renugadevi, Kannan Ramkumar, N. Raju, K. Adalarasu, Surya Prasath, Kumaravelu Narasimhan
2023 J jnl
IEEE Access
S. Saravanan, Kannan Ramkumar, Kumaravelu Narasimhan, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ajith Abraham
2023 J jnl
IEEE Access
M. Renugadevi, Kumaravelu Narasimhan, Chinthaginjala Venkata Ravikumar, Rajesh Anbazhagan, Giovanni Pau, Kannan Ramkumar, Mohamed Abbas, N. Raju, Kaveripaka Sathish, Prabu Sevugan
2022 J jnl
Math. Comput. Simul.
Gomathi Veerasamy, Kannan Ramkumar, RakeshKumar Siddharthan, Guruprasath Muralidharan, Venkatesh Sivanandam, Rengarajan Amirtharajan
2022 J jnl
Math. Comput. Simul.
Valarmathi Ramasamy, Kannan Ramkumar, Guruprasath Muralidharan, Rakesh Kumar Sidharthan, Rengarajan Amirtharajan
2021 J jnl
IEEE Trans. Educ.
Muthukumar Natarajan, Seshadhri Srinivasan, B. Subathra, Kannan Ramkumar
2020 J jnl
Int. J. Comput. Vis. Robotics
Rakesh Kumar Sidharthan, Kannan Ramkumar, Seshadhri Srinivasan
2019 J jnl
Future Gener. Comput. Syst.
Muthukumar Natarajan, Seshadhri Srinivasan, Kannan Ramkumar, Deepak Pal, Juri Vain, Srini Ramaswamy
2019 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Vignesh Narayanan, Sarangapani Jagannathan, Kannan Ramkumar
2017 J jnl
Pattern Recognit. Lett.
Arunkumar N., Kannan Ramkumar, Venkatraman V., Enas W. Abdulhay, Steven Lawrence Fernandes, Seifedine Nimer Kadry, Sophia Segal
2017 J jnl
Int. J. Adv. Intell. Paradigms
Venkatesh Sivanandam, Kannan Ramkumar, Seshadhri Srinivasan, Guruprasath Muralidharan
2016 J jnl
Int. J. Comput. Vis. Robotics
S. Rakesh Kumar, Kannan Ramkumar
2016 J jnl
Int. J. Simul. Process. Model.
Venkatesh Sivanandam, Kannan Ramkumar, Seshadhri Srinivasan, Guruprasath Muralidharan
2015 J jnl
IEEE Trans. Ind. Electron.
Xiaomin Lu, K. Lakshmi Varaha Iyer, Kaushik Mukherjee, Kannan Ramkumar, Narayan C. Kar
2015 conf
ITITS (2)
S. Rakesh Kumar, Kannan Ramkumar, Seshadhri Srinivasan, Valentina Emilia Balas, Fuqian Shi
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)