Madeline E. Smith

14 papers A* 3A 4B 1Journal 3Unranked 3
YearRankTypeTitle / Venue / Authors
2018 B conf
GROUP
Madeline E. Smith, Leo Ascenzi, Yingsi Qin, Ryan Wetsman
2017 A conf
SIGCSE
Amanda M. Holland-Minkley, Thomas E. Lombardi, Madeline E. Smith
2015 A* conf
CHI
Megan French, Madeline E. Smith, Jeremy P. Birnholtz, Jeff T. Hancock
2014 A conf
CSCW
Eden Litt, Erin L. Spottswood, Jeremy P. Birnholtz, Jeffrey T. Hancock, Madeline E. Smith, Lindsay Reynolds
2014 conf
CSCW Companion
Madeline E. Smith
2014 conf
CHI Extended Abstracts
Michael Massimi, Svetlana Yarosh, Madeline E. Smith, Joseph Jofish Kaye
2014 J jnl
Comput. Hum. Behav.
Madeline E. Smith, Jeffrey T. Hancock, Lindsay Reynolds, Jeremy P. Birnholtz
2014 conf
CSCW Companion
Madeline E. Smith, Asmaa Aljuhani, Jeremy P. Birnholtz, Jeffrey T. Hancock, Lindsay Reynolds
2013 J jnl
Comput. Hum. Behav.
Jeremy P. Birnholtz, Lindsay Reynolds, Madeline E. Smith, Jeffrey T. Hancock
2013 A conf
CSCW
Lindsay Reynolds, Madeline E. Smith, Jeremy P. Birnholtz, Jeffrey T. Hancock
2013 A* conf
CHI
Eric P. S. Baumer, Phil Adams, Vera D. Khovanskaya, Tony C. Liao, Madeline E. Smith, Victoria Schwanda Sosik, Kaiton Williams
2012 A conf
CSCW
Madeline E. Smith, Duyen T. Nguyen, Charles Lai, Gilly Leshed, Eric P. S. Baumer
2012 J jnl
Interactions
Jeremy P. Birnholtz, Jeffrey T. Hancock, Madeline E. Smith, Lindsay Reynolds
2011 A* conf
HRI
Madeline E. Smith, Sharon A. Stansfield, Carole W. Dennis
redb/extractors/decompiler/bninja/analysis/medium_level.py
← Index redb/extractors/decompiler/bninja/analysis/medium_level.py python
import time

from binaryninja import (
    MediumLevelILOperation as MLIL_OP,
)

try:
    from ..function_type import FunctionTypeAnalysis
    from ..similarity.minhasher import MinHasher, TokenKind
    from ..utils.hashes import calculate_sha256, calculate_tlsh
    from .medium_level_normalization import MediumLevelNormalization
except ImportError:
    from redb.extractors.decompiler.bninja.analysis.medium_level_normalization import MediumLevelNormalization
    from redb.extractors.decompiler.bninja.similarity.minhasher import MinHasher
    from redb.extractors.decompiler.bninja.function_type import FunctionTypeAnalysis
    from redb.extractors.decompiler.bninja.utils.hashes import calculate_sha256, calculate_tlsh


_MLIL_CALL_OPS = (
    MLIL_OP.MLIL_CALL,
    MLIL_OP.MLIL_CALL_SSA,
    MLIL_OP.MLIL_CALL_UNTYPED,
    MLIL_OP.MLIL_CALL_UNTYPED_SSA,
    MLIL_OP.MLIL_TAILCALL,
    MLIL_OP.MLIL_TAILCALL_SSA,
    MLIL_OP.MLIL_TAILCALL_UNTYPED,
    MLIL_OP.MLIL_TAILCALL_UNTYPED_SSA,
)

_MLIL_CONTROL_FLOW_OPS = (
    MLIL_OP.MLIL_IF,
    MLIL_OP.MLIL_GOTO,
    MLIL_OP.MLIL_JUMP,
    MLIL_OP.MLIL_JUMP_TO,
    MLIL_OP.MLIL_RET,
    MLIL_OP.MLIL_RET_HINT,
    MLIL_OP.MLIL_NORET,
) + _MLIL_CALL_OPS


class MediumLevelAnalysis:
    def __init__(self, function, bv, logger):
        self.function = function
        self.name = function.name
        self.start = function.start
        self.mlil_func = function.mlil
        self.bv = bv
        self.logger = logger
        self.errors = []

    def log_error(self, message, function_name, address, exception=None, error_location="unknown"):
        error_msg = f"Error in function {function_name} at {address}: {message}"
        if exception:
            error_msg += f" - {str(exception)}"
        self.logger.error(error_msg)

        error = {
            "function_name": function_name,
            "function_address": str(address),
            "error_location": error_location,
            "error_message": message,
            "error_details": str(exception) if exception else "",
            "error_type": type(exception).__name__ if exception else "Unknown",
            "timestamp": int(time.time() * 1000),
        }
        self.errors.append(error)

    def _collect_mlil_skeleton_and_typed(self):
        mlil = self.mlil_func
        if not mlil:
            return [], [], [], []

        start = self.start
        norm = MediumLevelNormalization()

        skeleton = []
        skeleton_with_addr = []
        typed = []
        typed_with_addr = []

        for il in mlil.instructions:
            skel_norm = norm.normalize_instruction_all_levels(il)
            typed_norm = norm.normalize_instr_with_operands(il)

            skeleton.append(skel_norm)
            typed.append(typed_norm)

            offset = il.address - start
            if offset < 0:
                offset = 0

            skeleton_with_addr.append((offset, skel_norm))
            typed_with_addr.append((offset, typed_norm))

        return skeleton, skeleton_with_addr, typed, typed_with_addr

    def analyze(self):
        (
            instr_skeleton,
            body_mlil_skeleton_vector,
            instr_typed,
            body_mlil_typed_vector,
        ) = self._collect_mlil_skeleton_and_typed()

        instr_skeleton_str = str(instr_skeleton)
        sha256_skeleton = calculate_sha256(instr_skeleton_str)
        tlsh_skeleton = calculate_tlsh(instr_skeleton_str)

        instr_typed_str = str(instr_typed)
        sha256_typed = calculate_sha256(instr_typed_str)
        tlsh_typed = calculate_tlsh(instr_typed_str)

        seed = 0xdeadbeef
        minhash_mlil_skeleton = MinHasher(seed, self.mlil_func, TokenKind.MLIL).calculateMinHash()
        minhash_mlil_typed = MinHasher(seed, self.mlil_func, TokenKind.TYPED_MLIL).calculateMinHash()

        medium_level_json = {
            "function_address": self.start,
            "body_mlil_skeleton_vector": body_mlil_skeleton_vector,
            "sha256_mlil_skeleton": sha256_skeleton,
            "tlsh_mlil_skeleton": tlsh_skeleton,
            "minhash_mlil_skeleton": minhash_mlil_skeleton,
            "body_mlil_typed_vector": body_mlil_typed_vector,
            "sha256_mlil_typed": sha256_typed,
            "tlsh_mlil_typed": tlsh_typed,
            "minhash_mlil_typed": minhash_mlil_typed,
        }

        return medium_level_json, self.errors