James P. Fairbanks

46 papers A* 1B 5C 1Misc 1Journal 27Unranked 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
Appl. Categorical Struct.
Benjamin Merlin Bumpus, Wilmer Leal, James P. Fairbanks, Martti Karvonen, Frédéric Simard
2025 J jnl
CoRR
Yichen Zhao, Tyler Hanks, Hans Riess, Samuel Cohen, Matthew T. Hale, James P. Fairbanks
2025 J jnl
CoRR
Mason Lary, Richard Samuelson, Alexander Wilentz, Alina Zare, Matthew Klawonn, James P. Fairbanks
2025 conf
CDC
Tyler Hanks, Hans Riess, Samuel Cohen, Trevor Gross, Matthew Hale, James P. Fairbanks
2025 J jnl
CoRR
Tyler Hanks, Hans Riess, Samuel Cohen, Trevor Gross, Matthew Hale, James P. Fairbanks
2025 J jnl
CoRR
Tyler Hanks, Cristian F. Nino, Joana Bou Barcelo, Austin Copeland, Warren E. Dixon, James P. Fairbanks
2025 A* conf
ICLR
Mason Lary, Richard Samuelson, Alexander Wilentz, Alina Zare, Matthew Klawonn, James P. Fairbanks
2025 J jnl
CoRR
Luke Morris, George Rauta, Kevin Carlson, James P. Fairbanks
2024 J jnl
Compositionality
Rebekah Aduddell, James P. Fairbanks, Amit Kumar, Pablo S. Ocal, Evan Patterson, Brandon T. Shapiro
2024 J jnl
CoRR
Luke Morris, Andrew Baas, Jesus Arias, Maia Gatlin, Evan Patterson, James P. Fairbanks
2024 J jnl
J. Comput. Sci.
Luke Morris, Andrew Baas, Jesus Arias, Maia Gatlin, Evan Patterson, James P. Fairbanks
2024 B conf
MFPS
Owen Lynch, Kristopher Brown, James P. Fairbanks, Evan Patterson
2024 conf
ACT
Tyler Hanks, Matthew Klawonn, James P. Fairbanks
2024 C conf
ACC
Tyler Hanks, Baike She, Evan Patterson, Matthew Hale, Matthew Klawonn, James P. Fairbanks
2024 J jnl
CoRR
Benjamin Merlin Bumpus, James P. Fairbanks, Will J. Turner
2024 J jnl
CoRR
Benjamin Merlin Bumpus, James P. Fairbanks, Martti Karvonen, Wilmer Leal, Frédéric Simard
2023 J jnl
CoRR
Angeline Aguinaldo, Evan Patterson, James P. Fairbanks, Jaime Ruiz
2023 conf
CDC
Baike She, Tyler Hanks, James P. Fairbanks, Matthew Hale
2023 J jnl
CoRR
Baike She, Tyler Hanks, James P. Fairbanks, Matthew T. Hale
2023 J jnl
CoRR
Ernst Althaus, Benjamin Merlin Bumpus, James P. Fairbanks, Daniel Rosiak
2023 J jnl
J. Log. Algebraic Methods Program.
Kristopher Brown, Evan Patterson, Tyler Hanks, James P. Fairbanks
2023 J jnl
Simul.
Robert K. Garrett Jr., James P. Fairbanks, Margaret L. Loper, James D. Moreland Jr.
2022 J jnl
CoRR
Sophie Libkind, Andrew Baas, Micah Halter, Evan Patterson, James P. Fairbanks
2022 J jnl
Compositionality
Evan Patterson, Owen Lynch, James P. Fairbanks
2022 J jnl
CoRR
Kristopher Brown, Tyler Hanks, James P. Fairbanks
2022 B conf
ICGT
Kristopher Brown, Evan Patterson, Tyler Hanks, James P. Fairbanks
2021 J jnl
CoRR
Evan Patterson, Owen Lynch, James P. Fairbanks
2021 J jnl
CoRR
Kristopher Brown, Evan Patterson, James P. Fairbanks
2021 conf
ACT
Sophie Libkind, Andrew Baas, Evan Patterson, James P. Fairbanks
2020 J jnl
CoRR
Micah Halter, Evan Patterson, Andrew Baas, James P. Fairbanks
2019 conf
ACT
Micah Halter, Christine Herlihy, James P. Fairbanks
2019 conf
MISDOOM
James P. Fairbanks, Natalie Fitch, Franklin Bradfield, Erica Briscoe
2019 J jnl
CoRR
Kun Cao, James P. Fairbanks
2018 conf
HPEC
Rohit Varkey Thankachan, Brian Paul Swenson, James P. Fairbanks
2017 conf
IPDPS Workshops
David Ediger, James P. Fairbanks
2017 Misc conf
ICCS
Eisha Nathan, Geoffrey Sanders, James P. Fairbanks, Van Emden Henson, David A. Bader
2017 conf
HPEC
Rohit Varkey Thankachan, Eric R. Hein, Brian Paul Swenson, James P. Fairbanks
2017 conf
COMPLEX NETWORKS
Eisha Nathan, James P. Fairbanks, David A. Bader
2017 J jnl
J. Complex Networks
James P. Fairbanks, David A. Bader, Geoffrey D. Sanders
2016 B conf
ASONAM
Anita Zakrzewska, Eisha Nathan, James P. Fairbanks, David A. Bader
2016
James P. Fairbanks
2016 B conf
ASONAM
James P. Fairbanks, Anita Zakrzewska, David A. Bader
2015 J jnl
Parallel Comput.
James P. Fairbanks, Ramakrishnan Kannan, Haesun Park, David A. Bader
2015 J jnl
CoRR
James P. Fairbanks, Geoffrey D. Sanders, David A. Bader
2013 B conf
ASONAM
James P. Fairbanks, David Ediger, Robert McColl, David A. Bader, Eric Gilbert
2011 J jnl
Electron. J. Comb.
James P. Fairbanks
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)