Jakob Jordan

23 papers A* 1Journal 18Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
Medical Image Anal.
Héloïse Bustin, Tom Meyer, Rolf Reiter, Jakob Jordan, Lars Walczak, Heiko Tzschätzsch, Ingolf Sack, Anja Hennemuth
2025 conf
MICCAI (3)
Héloïse Bustin, Tom Meyer, Jakob Jordan, Lars Walczak, Heiko Tzschätzsch, Ingolf Sack, Anja Hennemuth
2024 J jnl
CoRR
Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici
2024 J jnl
PLoS Comput. Biol.
Jakob Jordan, João Sacramento, Willem A. M. Wybo, Mihai A. Petrovici, Walter Senn
2024 J jnl
CoRR
Oleh Melnyk, Michael Quellmalz, Gabriele Steidl, Noah Jaitner, Jakob Jordan, Ingolf Sack
2023 J jnl
CoRR
Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan
2022 J jnl
Frontiers Neuroinformatics
Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, Johanna Senk
2022 J jnl
CoRR
Camille Gontier, Jakob Jordan, Mihai A. Petrovici
2022 J jnl
Parallel Comput.
Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel
2022 J jnl
CoRR
Alessandro Colombo, Simon Dold, Patrice Kolb, Nils Bernhardt, Patrick Behrens, Jonathan Correa, Stefan Düsterer, Benjamin Erk, Linos Hecht, Andrea Heilrath, Robert Irsig, Norman Iwe, Jakob Jordan, Björn Kruse, Bruno Langbehn, Bastian Manschwetus, Franklin Martinez, Karl-Heinz Meiwes-Broer, Kevin Oldenburg, Christopher Passow, Christian Peltz, Mario Sauppe, Fabian Seel, Rico Mayro P. Tanyag, Rolf Treusch, Anatoli Ulmer, Saida Walz, Thomas Fennel, Ingo Barke, Thomas Möller, Bernd von Issendorff, Daniela Rupp
2021 J jnl
CoRR
Henrik D. Mettler, Maximilian Schmidt, Walter Senn, Mihai A. Petrovici, Jakob Jordan
2021 conf
GECCO Companion
Henrik D. Mettler, Maximilian Schmidt, Walter Senn, Mihai A. Petrovici, Jakob Jordan
2021 J jnl
CoRR
Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
2021 A* conf
NeurIPS
Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
2021 J jnl
CoRR
Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan
2021 J jnl
CoRR
Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel
2021 J jnl
ERCIM News
Henrik D. Mettler, Virginie Sabado, Walter Senn, Mihai A. Petrovici, Jakob Jordan
2020 conf
NICE
Jakob Jordan, Mihai A. Petrovici, Walter Senn, João Sacramento
2020 J jnl
Frontiers Neuroinformatics
Jakob Jordan, Moritz Helias, Markus Diesmann, Susanne Kunkel
2019 J jnl
Frontiers Comput. Neurosci.
Jakob Jordan, Philipp Weidel, Abigail Morrison
2018 J jnl
Frontiers Neuroinformatics
Jakob Jordan, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, Susanne Kunkel
2018 J jnl
Frontiers Neuroinformatics
Jakob Jordan, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, Susanne Kunkel
2018
Jakob Jordan
redb/extractors/decompiler/bninja/analysis/cfg.py
← Index redb/extractors/decompiler/bninja/analysis/cfg.py python
from binaryninja.enums import LowLevelILOperation as LLIL_OP

# Support both package and standalone imports
try:
    from . import cfg_features
except ImportError:
    from redb.extractors.decompiler.bninja.analysis import cfg_features


# ---------------------------------------------------------------------------
# Task 2.2: Build LLIL operation maps at import time using real enum values
# ---------------------------------------------------------------------------

# Prime product map: LLIL operation integer value -> small prime
cfg_features.LLIL_OP_PRIMES = {
    # SET_REG, SET_REG_SPLIT
    LLIL_OP.LLIL_SET_REG.value: 2,
    LLIL_OP.LLIL_SET_REG_SPLIT.value: 2,
    # SET_FLAG
    LLIL_OP.LLIL_SET_FLAG.value: 3,
    # LOAD
    LLIL_OP.LLIL_LOAD.value: 5,
    # STORE
    LLIL_OP.LLIL_STORE.value: 7,
    # PUSH, POP
    LLIL_OP.LLIL_PUSH.value: 11,
    LLIL_OP.LLIL_POP.value: 13,
    # CALL, TAILCALL, SYSCALL
    LLIL_OP.LLIL_CALL.value: 17,
    LLIL_OP.LLIL_TAILCALL.value: 17,
    LLIL_OP.LLIL_SYSCALL.value: 19,
    # RET, NORET
    LLIL_OP.LLIL_RET.value: 23,
    LLIL_OP.LLIL_NORET.value: 23,
    # IF, GOTO
    LLIL_OP.LLIL_IF.value: 29,
    LLIL_OP.LLIL_GOTO.value: 31,
    # ADD, SUB
    LLIL_OP.LLIL_ADD.value: 37,
    LLIL_OP.LLIL_SUB.value: 41,
    # AND, OR, XOR
    LLIL_OP.LLIL_AND.value: 43,
    LLIL_OP.LLIL_OR.value: 47,
    LLIL_OP.LLIL_XOR.value: 53,
    # LSL, LSR, ASR, ROL, ROR
    LLIL_OP.LLIL_LSL.value: 59,
    LLIL_OP.LLIL_LSR.value: 61,
    LLIL_OP.LLIL_ASR.value: 67,
    LLIL_OP.LLIL_ROL.value: 71,
    LLIL_OP.LLIL_ROR.value: 73,
    # MUL, DIVU, DIVS, MODU, MODS
    LLIL_OP.LLIL_MUL.value: 79,
    LLIL_OP.LLIL_DIVU.value: 83,
    LLIL_OP.LLIL_DIVS.value: 83,
    LLIL_OP.LLIL_MODU.value: 89,
    LLIL_OP.LLIL_MODS.value: 89,
    # NEG, NOT
    LLIL_OP.LLIL_NEG.value: 97,
    LLIL_OP.LLIL_NOT.value: 101,
    # CMP_E, CMP_NE, CMP_SLT, CMP_ULT, CMP_SLE, CMP_ULE
    # CMP_SGT, CMP_UGT, CMP_SGE, CMP_UGE
    LLIL_OP.LLIL_CMP_E.value: 103,
    LLIL_OP.LLIL_CMP_NE.value: 103,
    LLIL_OP.LLIL_CMP_SLT.value: 107,
    LLIL_OP.LLIL_CMP_ULT.value: 107,
    LLIL_OP.LLIL_CMP_SLE.value: 109,
    LLIL_OP.LLIL_CMP_ULE.value: 109,
    LLIL_OP.LLIL_CMP_SGT.value: 113,
    LLIL_OP.LLIL_CMP_UGT.value: 113,
    LLIL_OP.LLIL_CMP_SGE.value: 127,
    LLIL_OP.LLIL_CMP_UGE.value: 127,
    # NOP
    LLIL_OP.LLIL_NOP.value: 1,
    # SX, ZX, LOW_PART, BOOL_TO_INT
    LLIL_OP.LLIL_SX.value: 131,
    LLIL_OP.LLIL_ZX.value: 137,
    LLIL_OP.LLIL_LOW_PART.value: 139,
    LLIL_OP.LLIL_BOOL_TO_INT.value: 149,
    # JUMP, JUMP_TO
    LLIL_OP.LLIL_JUMP.value: 151,
    LLIL_OP.LLIL_JUMP_TO.value: 151,
}

# Category map: LLIL operation integer value -> category index
_ARITHMETIC = {
    LLIL_OP.LLIL_ADD, LLIL_OP.LLIL_ADC, LLIL_OP.LLIL_SUB, LLIL_OP.LLIL_SBB,
    LLIL_OP.LLIL_MUL, LLIL_OP.LLIL_MULU_DP, LLIL_OP.LLIL_MULS_DP,
    LLIL_OP.LLIL_DIVU, LLIL_OP.LLIL_DIVU_DP, LLIL_OP.LLIL_DIVS,
    LLIL_OP.LLIL_DIVS_DP, LLIL_OP.LLIL_MODU, LLIL_OP.LLIL_MODS,
    LLIL_OP.LLIL_NEG,
}
_LOGIC = {
    LLIL_OP.LLIL_AND, LLIL_OP.LLIL_OR, LLIL_OP.LLIL_XOR, LLIL_OP.LLIL_NOT,
    LLIL_OP.LLIL_LSL, LLIL_OP.LLIL_LSR, LLIL_OP.LLIL_ASR,
    LLIL_OP.LLIL_ROL, LLIL_OP.LLIL_RLC, LLIL_OP.LLIL_ROR, LLIL_OP.LLIL_RRC,
}
_TRANSFER = {
    LLIL_OP.LLIL_SET_REG, LLIL_OP.LLIL_SET_REG_SPLIT, LLIL_OP.LLIL_SET_FLAG,
    LLIL_OP.LLIL_GOTO, LLIL_OP.LLIL_IF, LLIL_OP.LLIL_JUMP, LLIL_OP.LLIL_JUMP_TO,
    LLIL_OP.LLIL_RET, LLIL_OP.LLIL_NORET, LLIL_OP.LLIL_PUSH, LLIL_OP.LLIL_POP,
}
_CALL = {
    LLIL_OP.LLIL_CALL, LLIL_OP.LLIL_TAILCALL, LLIL_OP.LLIL_SYSCALL,
}
_COMPARISON = {
    LLIL_OP.LLIL_CMP_E, LLIL_OP.LLIL_CMP_NE,
    LLIL_OP.LLIL_CMP_SLT, LLIL_OP.LLIL_CMP_ULT,
    LLIL_OP.LLIL_CMP_SLE, LLIL_OP.LLIL_CMP_ULE,
    LLIL_OP.LLIL_CMP_SGE, LLIL_OP.LLIL_CMP_UGE,
    LLIL_OP.LLIL_CMP_SGT, LLIL_OP.LLIL_CMP_UGT,
    LLIL_OP.LLIL_TEST_BIT, LLIL_OP.LLIL_FLAG_COND,
}
_MEMORY = {
    LLIL_OP.LLIL_LOAD, LLIL_OP.LLIL_STORE,
}

cfg_features.LLIL_OP_CATEGORIES = {}
for _op in _ARITHMETIC:
    cfg_features.LLIL_OP_CATEGORIES[_op.value] = cfg_features.CAT_ARITHMETIC
for _op in _LOGIC:
    cfg_features.LLIL_OP_CATEGORIES[_op.value] = cfg_features.CAT_LOGIC
for _op in _TRANSFER:
    cfg_features.LLIL_OP_CATEGORIES[_op.value] = cfg_features.CAT_TRANSFER
for _op in _CALL:
    cfg_features.LLIL_OP_CATEGORIES[_op.value] = cfg_features.CAT_CALL
for _op in _COMPARISON:
    cfg_features.LLIL_OP_CATEGORIES[_op.value] = cfg_features.CAT_COMPARISON
for _op in _MEMORY:
    cfg_features.LLIL_OP_CATEGORIES[_op.value] = cfg_features.CAT_MEMORY

# Set of CALL operation values for counting
_CALL_OPS = {op.value for op in _CALL}


# ---------------------------------------------------------------------------
# Task 2.1 + 2.3: Rewritten CFGAnalysis
# ---------------------------------------------------------------------------

class CFGAnalysis:
    def __init__(self, function, llil_function=None):
        self.function = function
        self.llil_function = llil_function

    def extract_function_cfg(self):
        """Extract function-level CFG features as a flat dictionary."""

        if self.function is None:
            return None

        blocks = list(self.function.basic_blocks)
        if not blocks:
            return None

        n = len(blocks)

        # 1. Build index-based adjacency from Binary Ninja blocks
        addr_to_idx = {b.start: i for i, b in enumerate(blocks)}
        successors = [[] for _ in range(n)]
        predecessors = [[] for _ in range(n)]
        for i, block in enumerate(blocks):
            for edge in block.outgoing_edges:
                if edge.target is None:
                    continue
                target_idx = addr_to_idx.get(edge.target.start)
                if target_idx is not None:
                    successors[i].append(target_idx)
                    predecessors[target_idx].append(i)

        # 2. BFS order (reusable across multiple features)
        bfs = cfg_features.bfs_order(successors, n)

        # 3. Collect per-block LLIL operations (for prime product + ACFG features)
        block_llil_ops = self._collect_block_llil_ops(blocks, addr_to_idx, n)
        all_llil_ops = [op for block_ops in block_llil_ops for op in block_ops]

        # 4. Structural counts
        edge_count = sum(len(s) for s in successors)
        total_llil = sum(len(ops) for ops in block_llil_ops)
        call_count = sum(
            1 for ops in block_llil_ops for op in ops
            if op in _CALL_OPS
        )

        # 5. Compute all features
        bb_features = cfg_features.build_block_features(block_llil_ops, successors, n)

        return {
            "cfg_topology_hash": cfg_features.compute_topology_hash(successors, bfs, n),
            "block_count": n,
            "edge_count": edge_count,
            "llil_total_operations": total_llil,
            "call_count": call_count,
            "cyclomatic_complexity": edge_count - n + 2,
            "loop_count": cfg_features.count_back_edges(successors, n),
            "max_depth": cfg_features.bfs_max_depth(successors, n),
            "max_fan_out": max((len(s) for s in successors), default=0),
            "md_index_topdown": cfg_features.compute_md_index_topdown(successors, predecessors, bfs),
            "md_index_bottomup": cfg_features.compute_md_index_bottomup(successors, predecessors, n),
            "prime_product_llil": cfg_features.compute_prime_product(all_llil_ops),
            "cfg_feature_tlsh": cfg_features.compute_cfg_feature_tlsh(bb_features, bfs),
            "wl_minhash": cfg_features.compute_wl_minhash(successors, predecessors, bb_features, n),
            "bb_features": bb_features,
            "cfg_adjacency": cfg_features.pack_adjacency(successors),
        }

    def _collect_block_llil_ops(self, blocks, addr_to_idx, n):
        """
        Collect LLIL operation integers per native basic block.
        Walks the full expression tree of each instruction so that
        nested operations (e.g. ADD inside SET_REG) are captured.
        Returns list of n lists, one per block.
        """
        block_ops = [[] for _ in range(n)]

        if self.llil_function is None:
            return block_ops

        try:
            for llil_block in self.llil_function.basic_blocks:
                # Map LLIL block to native block via source_block
                if llil_block.source_block is not None:
                    native_idx = addr_to_idx.get(llil_block.source_block.start)
                    if native_idx is not None:
                        for instr in llil_block:
                            self._walk_llil_ops(instr, block_ops[native_idx])
        except Exception:
            pass  # Return empty ops — LLIL-dependent fields will be 0/null

        return block_ops

    @staticmethod
    def _walk_llil_ops(expr, ops_list):
        """Collect operation values from an LLIL expression tree iteratively."""
        stack = [expr]
        while stack:
            node = stack.pop()
            if hasattr(node, 'operation'):
                ops_list.append(node.operation.value)
            if hasattr(node, 'operands'):
                for operand in node.operands:
                    if hasattr(operand, 'operation'):
                        stack.append(operand)