Walter Schneider

32 papers B 3Journal 16Unranked 13
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Hwihun Jeong, Qiang Liu, Kathryn E. Keenan, Elisabeth A. Wilde, Walter Schneider, Sudhir K. Pathak, Anthony Zuccolotto, Lauren J. O'Donnell, Lipeng Ning, Yogesh Rathi
2024 J jnl
Signal Image Video Process.
Abdul Halim, B. V. Rathish Kumar, Ajay Niranjan, Aditya Nigam, Walter Schneider, Chirag Kamal Ahuja, Sudhir K. Pathak
2024 conf
CDMRI@MICCAI
Nancy Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E. Kelly, Sila Genc, Joseph Yuan-Mou Yang, Ye Wu, Nagesh Adluru, Vishwesh Nath, Sudhir K. Pathak, Walter Schneider, Anurag Gade, William Consagra, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland, Tomasz Pieciak, Dominika Ciupek, Antonio Tristán-Vega, Santiago Aja-Fernández, Maciej Malawski, Gani Ouedraogo, Julia Machnio, Paul M. Thompson, Neda Jahanshad, Eleftherios Garyfallidis, Bennett A. Landman
2024 J jnl
CoRR
Nancy R. Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E. Kelly, Sila Genc, Jian Chen, Joseph Yuan-Mou Yang, Ye Wu, Yifei He, Jiawei Zhang, Qingrun Zeng, Fan Zhang, Nagesh Adluru, Vishwesh Nath, Sudhir K. Pathak, Walter Schneider, Anurag Gade, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland, Tomasz Pieciak, Dominika Ciupek, Antonio Tristán-Vega, Santiago Aja-Fernández, Maciej Malawski, Gani Ouedraogo, Julia Machnio, Christian Ewert, Paul M. Thompson, Neda Jahanshad, Eleftherios Garyfallidis, Bennett A. Landman
2023 conf
ISBI
Ranjeet Ranjan Jha, Hritik Gupta, Sudhir K. Pathak, Ankita Joshi, Walter Schneider, B. V. Rathish Kumar, Arnav Bhavsar, Aditya Nigam
2023 J jnl
Comput. Methods Programs Biomed.
Ranjeet Ranjan Jha, B. V. Rathish Kumar, Sudhir K. Pathak, Walter Schneider, Arnav Bhavsar, Aditya Nigam
2022 conf
ISBI
Ranjeet Ranjan Jha, Sudhir K. Pathak, Walter Schneider, B. V. Rathish Kumar, Arnav Bhavsar, Aditya Nigam
2021 B conf
MDM
Constantinos Costa, Brian T. Nixon, Sayantani Bhattacharjee, Benjamin Graybill, Demetrios Zeinalipour-Yazti, Walter Schneider, Panos K. Chrysanthis
2021 J jnl
NeuroImage
Susie Yi Huang, Thomas Witzel, Boris Keil, Alina Scholz, Mathias Davids, Peter Dietz, Elmar Rummert, Rebecca Ramb, John E. Kirsch, Anastasia Yendiki, Qiuyun Fan, Qiyuan Tian, Gabriel Ramos-Llordén, Hong-Hsi Lee, Aapo Nummenmaa, Berkin Bilgic, Kawin Setsompop, Fuyixue Wang, Alexandru Vlad Avram, Michal E. Komlosh, Dan Benjamini, Kulam Najmudeen Magdoom, Sudhir K. Pathak, Walter Schneider, Dmitry S. Novikov, Els Fieremans, Slimane Tounekti, Choukri Mekkaoui, Jean Augustinack, Daniel R. Berger, Alexander Shapson-Coe, Jeff Lichtman, Peter J. Basser, Lawrence L. Wald, Bruce R. Rosen
2021 conf
ISBI
Ranjeet Ranjan Jha, Hritik Gupta, Sudhir K. Pathak, Walter Schneider, B. V. Rathish Kumar, Arnav Bhavsar, Aditya Nigam
2021 B conf
MDM
Brian T. Nixon, Sayantani Bhattacharjee, Benjamin Graybill, Constantinos Costa, Sudhir K. Pathak, Walter Schneider, Panos K. Chrysanthis
2021 J jnl
Microelectron. J.
Walter Schneider
2020 conf
EMBC
Ranjeet Ranjan Jha, Aditya Nigam, Arnav Bhavsar, Sudhir K. Pathak, Walter Schneider, Rathish Kumar
2019 conf
ISBI
Kristofer Pomiecko, Carson Sestili, Kate Fissell, Sudhir K. Pathak, David O. Okonkwo, Walter Schneider
2018 J jnl
CoRR
Walter Schneider
2018 J jnl
NeuroImage
Qiuyun Fan, Aapo Nummenmaa, Barbara Wichtmann, Thomas Witzel, Choukri Mekkaoui, Walter Schneider, Lawrence L. Wald, Susie Yi Huang
2017 J jnl
NeuroImage
Ravi D. Mill, Anto Bagic, Andreea C. Bostan, Walter Schneider, Michael W. Cole
2014 conf
InMed
Catarina Guise, Raul Fangueiro, João Miguel Nóbrega, Walter Schneider
2013 conf
XSEDE
Don Krieger, Malcolm McNeil, Jinyin Zhang, Walter Schneider, Xin Li, David O. Okonkwo
2012 J jnl
NeuroImage
Kevin Jarbo, Timothy D. Verstynen, Walter Schneider
2011 B conf
CogSci
Jarrod Moss, Christian D. Schunn, Walter Schneider, Danielle S. McNamara
2011 J jnl
NeuroImage
Jarrod Moss, Christian D. Schunn, Walter Schneider, Danielle S. McNamara, Kurt VanLehn
2010 J jnl
NeuroImage
Michael W. Cole, Sudhir K. Pathak, Walter Schneider
2009 conf
HEALTHINF
Heinz-Josef Eikerling, Gernot Gräfe, Florian Röhr, Walter Schneider
2009 conf
AAAI Fall Symposium: Biologically Inspired Cognitive Architectures
Walter Schneider, Sudhir K. Pathak, Jeffrey S. Phillips, Micahel Cole
2008 conf
AAAI Fall Symposium: Biologically Inspired Cognitive Architectures
Walter Schneider, Michael W. Cole, Sudhir K. Pathak
2007 J jnl
NeuroImage
Michael W. Cole, Walter Schneider
2006 conf
ITSC
Walter Schneider, Marco Reitmeier, Matthias Kastner
2003 J jnl
Cogn. Sci.
Walter Schneider, Jason M. Chein
2003 J jnl
Neuroinformatics
Kate Fissell, Eugene Tseytlin, Daniel Cunningha, Karthickeyan Iyer, Cameron S. Carter, Walter Schneider, Jonathan D. Cohen
1995 J jnl
Int. J. Imaging Syst. Technol.
Michael S. Worden, Walter Schneider
1994 conf
ICIP (3)
Douglas C. Noll, Walter Schneider
redb/extractors/decompiler/bninja/analysis/low_level.py
← Index redb/extractors/decompiler/bninja/analysis/low_level.py python
import time

from binaryninja import (
    LowLevelILInstruction,
)
from binaryninja import (
    LowLevelILOperation as LLIL_OP,
)
from binaryninja.lowlevelil import (
    LowLevelILAdd,
    LowLevelILConst,
    LowLevelILConstPtr,
    LowLevelILLoad,
    LowLevelILLsl,
    LowLevelILMul,
    LowLevelILPop,
    LowLevelILPush,
    LowLevelILReg,
    LowLevelILStore,
    LowLevelILSub,
)

# Support both package and standalone imports
try:
    from ..function_type import FunctionTypeAnalysis
    from ..similarity.minhasher import MinHasher, TokenKind
    from ..utils.hashes import calculate_sha256, calculate_tlsh
    from .low_level_normalization import LowLevelNormalization

except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.analysis.low_level_normalization import LowLevelNormalization
    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

class LowLevelAnalysis:
    def __init__(self, function, bv, logger):
        self.function = function
        self.name = function.name
        self.start = function.start
        self.llil_func = function.llil
        self.bv = bv
        self.logger = logger
        self.errors = []

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

        # Add to errors list
        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 count_control_flow_instructions(self):
        if self.llil_func is None:
            return 0

        count = 0
        for basic_block in self.llil_func.basic_blocks:
            for ins in basic_block:
                op = ins.operation
                if op in (
                    LLIL_OP.LLIL_IF,
                    LLIL_OP.LLIL_GOTO,
                    LLIL_OP.LLIL_JUMP,
                    LLIL_OP.LLIL_JUMP_TO,
                    LLIL_OP.LLIL_CALL,
                    LLIL_OP.LLIL_CALL_SSA,
                ):
                    count += 1

        return count

    def collect_memory_patterns(self):
        """ """
        patterns = set()

        try:
            llil = self.llil_func
            arch = self.bv.arch
            sp_name = arch.stack_pointer if arch and arch.stack_pointer else "sp"

            def analyze_addr(addr_expr, might_be_direct):
                """
                Visit the expression for the address and understand whether it has a direct, scaled, base offset, etc.
                access to memory
                """
                found = {
                    "direct": False,
                    "scaled": False,
                    "base_off": False,
                    "stack": False,
                    "string": False,
                }

                def addr_cb(n):
                    # stack (SP/BP-like)
                    match n:
                        case LowLevelILReg(src=reg):
                            if reg == sp_name:
                                found["stack"] = True

                        case LowLevelILConst() | LowLevelILConstPtr():
                            if might_be_direct:
                                found["direct"] = True

                        # base +/- const
                        case (
                            LowLevelILAdd(left=l, right=r)
                            | LowLevelILSub(left=l, right=r)
                        ):
                            l_is_reg = isinstance(l, LowLevelILReg)
                            r_is_reg = isinstance(r, LowLevelILReg)
                            l_is_cst = isinstance(
                                l, (LowLevelILConst, LowLevelILConstPtr)
                            )
                            r_is_cst = isinstance(
                                r, (LowLevelILConst, LowLevelILConstPtr)
                            )
                            if (l_is_reg and r_is_cst) or (r_is_reg and l_is_cst):
                                found["base_off"] = True

                        # scaled index (index*scale) or shift (index << k)
                        case LowLevelILMul(left=l, right=r):
                            if (
                                isinstance(l, LowLevelILReg)
                                and isinstance(r, LowLevelILConst)
                            ) or (
                                isinstance(r, LowLevelILReg)
                                and isinstance(l, LowLevelILConst)
                            ):
                                found["scaled"] = True

                        case LowLevelILLsl(left, right):
                            if isinstance(left, LowLevelILReg) and isinstance(
                                right, LowLevelILConst
                            ):
                                found["scaled"] = True

                    return None

                _ = list(addr_expr.traverse(addr_cb))

                if found["direct"]:
                    patterns.add("MEM_DIRECT")
                if found["scaled"]:
                    patterns.add("MEM_SCALED_INDEX")
                if found["base_off"]:
                    patterns.add("MEM_BASE_OFFSET")
                if found["stack"]:
                    patterns.add("MEM_STACK")
                elif found["string"]:
                    patterns.add("MEM_STRING")

            def func_cb(i):
                match i:
                    case LowLevelILPush(src=addr):
                        analyze_addr(addr, False)
                    case LowLevelILPop(src=addr):
                        analyze_addr(addr, False)
                    case LowLevelILLoad(src=addr):
                        analyze_addr(addr, True)
                    case LowLevelILStore(dest=addr, src=_):
                        analyze_addr(addr, True)

                return None

            # complete visit for the single instruction
            _ = list(llil.traverse(func_cb))

            return sorted(patterns)

        except Exception as e:
            self.log_error(
                "Failed to collect memory patterns via LLIL.traverse",
                self.name,
                self.start,
                e,
                "collect_memory_patterns",
            )
            return []

    def collect_register_usage(self):
        """
        Collect frequencies for register usage
        """
        try:
            llil = self.llil_func

            register_usage = {}

            def inc(reg, kind):
                if reg is None:
                    return
                entry = register_usage.setdefault(reg, {"reads": 0, "writes": 0})
                entry[kind] += 1

            if not llil:
                return {}, 0, 0

            for top_il in llil.instructions:
                registers_read = self.function.get_regs_read_by(
                    top_il.address, self.bv.arch
                )
                registers_write = self.function.get_regs_written_by(
                    top_il.address, self.bv.arch
                )

                for reg_read in registers_read:
                    inc(reg_read, "reads")

                for reg_write in registers_write:
                    inc(reg_write, "writes")

            total_reads = sum(entry["reads"] for entry in register_usage.values())
            total_writes = sum(entry["writes"] for entry in register_usage.values())

            return register_usage, total_reads, total_writes

        except Exception as e:
            self.log_error(
                "Failed to collect register usage via LLIL.traverse",
                self.name,
                self.start,
                e,
                "collect_register_usage",
            )
            return {}, 0, 0

    def _classify_address(self, bv, addr):
        """
        Classification of the address
        """
        info = {
            "address": addr,
            "section": None,
            "segment_writable": None,
            "symbol": None,
            "kind": None,  # "string", "function_ptr", "data_var", "symbol", "unknown"
            "datatype": None,  # es. "char *", "int32_t", "my_struct", ...
            "note": None,
        }

        # section / segment
        sec = bv.get_section_at(addr)
        seg = bv.get_segment_at(addr)
        if sec:
            info["section"] = sec.name
        if seg:
            info["segment_writable"] = bool(seg.writable)

        sym = bv.get_symbol_at(addr)
        if sym:
            info["symbol"] = sym.full_name

        # function pointer
        try:
            fns = list(bv.get_functions_at(addr))
        except Exception:
            # some versions have get_function_at(addr) that returns a single object or None
            fns = [bv.get_function_at(addr)] if hasattr(bv, "get_function_at") else []
        fns = [f for f in fns if f]
        if fns:
            info["kind"] = "function_ptr"
            info["datatype"] = "func"
            info["note"] = f"points to function {fns[0].name}"
            return info

        # string
        sref = bv.get_string_at(addr)
        if sref:
            info["kind"] = "string"
            # sref.type:
            info["datatype"] = (
                getattr(sref, "type", None).__class__.__name__
                if hasattr(sref, "type")
                else "string"
            )
            return info

        # data typed variable
        dv = bv.get_data_var_at(addr)
        if dv:
            info["kind"] = "data_var"
            info["datatype"] = str(dv.type) if getattr(dv, "type", None) else None
            if getattr(dv, "name", None):
                info["symbol"] = dv.name if not info["symbol"] else info["symbol"]
            return info

        # only symbol (no data var)
        if sym and not info["kind"]:
            info["kind"] = "symbol"
            return info

        # unknown
        info["kind"] = "unknown"
        return info

    def count_data_references(self):
        """Count the number of data references in a function using LLIL."""
        count = 0

        if self.llil_func is None:
            return 0

        try:
            # Iterate LLIL basic blocks and instructions
            for instr in self.llil_func.instructions:
                instr_str = str(instr)
                logged = False
                src = None

                # Check for constant dereferencing or symbolic refs
                if hasattr(instr, "src"):
                    src = instr.src
                    if isinstance(src, (LowLevelILConstPtr, LowLevelILConst)):
                        count += 1
                        logged = True

                if not logged and "_" in instr_str:
                    count += 1

                # If src is a pointer constant, check if it lands in a writable data segment
                if src is not None and isinstance(src, LowLevelILConstPtr):
                    addr = src.constant
                    segment = self.bv.get_segment_at(addr)
                    if segment and segment.writable:
                        count += 1

        except Exception as e:
            self.logger.warning(
                f"Failed to use LLIL for counting data references in "
                f"{self.name} at {self.start}: {e}"
            )
        return count

    def compute_num_calls(self):
        c = 0

        if not self.llil_func:
            return 0

        for instr in self.llil_func.instructions:
            if instr.operation in (LLIL_OP.LLIL_CALL, LLIL_OP.LLIL_TAILCALL):
                c += 1
        return c

    def compute_max_block_size(self):
        """Compute the maximum basic block size in a function."""
        if self.llil_func is None:
            return 0

        max_size = 0
        for block in self.llil_func.basic_blocks:
            try:
                # Count instructions in this block using the direct length approach
                # This avoids UTF-8 decoding issues entirely
                block_size = block.instruction_count
                max_size = max(max_size, block_size)
            except Exception as e:
                self.log_error(
                    f"[HandledError] computing max block size: {e}",
                    self.name,
                    self.start,
                    e,
                    "compute_max_block_size",
                )
        return max_size

    def estimate_stack_size(self):
        """Estimate the stack size used by a function."""
        try:
            # Binary Ninja provides a stack adjustment value for functions
            # Need to convert OffsetWithConfidence to a plain integer
            stack_adjust = self.function.stack_adjustment
            if hasattr(stack_adjust, "value"):  # Handle OffsetWithConfidence objects
                return stack_adjust.value
            return stack_adjust
        except Exception as e:
            self.log_error(
                "Failed to estimate stack size",
                self.name,
                self.start,
                e,
                "estimate_stack_size",
            )
            return -1

    def collect_instruction_types(self):
        def iter_llil_tree(root_il):
            stack = [root_il]
            while stack:
                il_single_op = stack.pop()
                if not isinstance(il_single_op, LowLevelILInstruction):
                    continue
                yield il_single_op
                for il_operand in il_single_op.operands:
                    if isinstance(il_operand, LowLevelILInstruction):
                        stack.append(il_operand)
                    elif isinstance(il_operand, (list, tuple)):
                        for sub in il_operand:
                            if isinstance(sub, LowLevelILInstruction):
                                stack.append(sub)

        type_frequencies = {}
        try:
            if self.llil_func is None:
                return type_frequencies

            il_func = self.llil_func

            for top_il in il_func.instructions:
                for il in iter_llil_tree(top_il):
                    op = getattr(il, "operation", None)
                    if op is None:
                        continue

                    category = str(op)

                    if category in type_frequencies:
                        type_frequencies[category] += 1
                    else:
                        type_frequencies[category] = 1

        except Exception as e:
            self.log_error(
                "Failed to collect LLIL instruction types",
               self.name,
                self.start,
                e,
                "collect_instruction_types_llil",
            )

        return type_frequencies

    def _collect_low_level_with_type(self):
        llil = self.llil_func
        if not llil:
            return [], []

        start = self.start

        low_level = LowLevelNormalization()

        instrs_with_addr = []

        for il in llil.instructions:
            norm = low_level.normalize_instr_with_operands(il)

            # Clamp negative offsets to 0 for UInt32 compatibility.
            # Negative offsets (instruction before function start) may occur with
            # overlapping functions or tail-calls in obfuscated/malware binaries.
            # Multiple instructions at offset 0 indicates this anomaly and can be
            # queried to identify such samples easily than by checking logs.
            # Triggered by 590ecad54cd9e1c8681509420ad56edde8b064ffbf884ce6cd8dd28eebb95ae1
            offset = il.address - start
            if offset < 0:
                offset = 0

            instrs_with_addr.append((offset, norm))

        return instrs_with_addr

    def _collect_low_level_and_with_addr(self):
        llil = self.llil_func
        if not llil:
            return [], []

        start = self.start

        low_level = LowLevelNormalization()

        instrs = []
        instrs_with_addr = []

        for il in llil.instructions:
            norm = low_level.normalize_instruction_all_levels(il)

            instrs.append(norm)

            # Clamp negative offsets to 0 for UInt32 compatibility.
            # Negative offsets (instruction before function start) may occur with
            # overlapping functions or tail-calls in obfuscated/malware binaries.
            # Multiple instructions at offset 0 indicates this anomaly and can be
            # queried to identify such samples easily than by checking logs.
            # Triggered by 590ecad54cd9e1c8681509420ad56edde8b064ffbf884ce6cd8dd28eebb95ae1
            offset = il.address - start
            if offset < 0:
                offset = 0

            instrs_with_addr.append((offset, norm))

        return instrs, instrs_with_addr


    def analyze(self):
        registers_uses, total_reads, total_written = self.collect_register_usage()
        instr_low_level, body_llil_vector = self._collect_low_level_and_with_addr()
        instr_low_level_str = str(instr_low_level)
        instructions_low_level = calculate_sha256(instr_low_level_str)
        instructions_low_level_tlsh = calculate_tlsh(instr_low_level_str)

        instr_typed_llil = self._collect_low_level_with_type()
        instr_typed_low_level_str = str(instr_typed_llil)
        instructions_typed_low_level = calculate_sha256(instr_typed_low_level_str)
        instructions_typed_low_level_tlsh = calculate_tlsh(instr_typed_low_level_str)

        seed = 0xdeadbeef
        minhash_llil_skeleton = MinHasher(seed, self.llil_func, TokenKind.LLIL).calculateMinHash()
        minhash_llil_typed = MinHasher(seed, self.llil_func, TokenKind.TYPED_LLIL).calculateMinHash()

        low_level_json = {
            "function_address": self.start,
            "function_type": FunctionTypeAnalysis(self.function)
            .get_function_type()
            .name,
            "body_llil_vector": body_llil_vector,
            "sha256_llil": instructions_low_level,
            "tlsh_llil": instructions_low_level_tlsh,
            "minhash_llil_skeleton": minhash_llil_skeleton,
            "instructions_types_llil": list(self.collect_instruction_types()),
            "instruction_typed_llil": instructions_typed_low_level,
            "tlsh_instruction_typed_llil": instructions_typed_low_level_tlsh,
            "minhash_llil_typed": minhash_llil_typed,
            "control_flow_count_llil": self.count_control_flow_instructions(),
            "memory_access_pattern_llil": self.collect_memory_patterns(),
            "register_usage": registers_uses,
            "total_reg_reads": total_reads,
            "total_reg_written": total_written,
            "data_references_count": self.count_data_references(),
            "max_block_size": self.compute_max_block_size(),
            "num_calls": self.compute_num_calls(),
            "stack_size": self.estimate_stack_size(),
        }

        return low_level_json, self.errors