Om Prakash Verma

81 papers Misc 1Journal 65Unranked 15
YearRankTypeTitle / Venue / Authors
2026 J jnl
Signal Image Video Process.
Amit Kumar Dwivedi, Om Prakash Verma, Sachin Taran
2026 J jnl
Digit. Signal Process.
Amit Kumar Dwivedi, Om Prakash Verma, Sachin Taran
2026 J jnl
IEICE Trans. Fundam. Electron. Commun. Comput. Sci.
Nidhi Beniwal, Om Prakash Verma
2026 J jnl
Adv. Intell. Syst.
Pratima Kumari, Sachin Kadian, Mukund Vora, Pradyumna Vemuri, Sumit Kumar, Om Prakash Verma, Roger J. Narayan
2025 J jnl
Multim. Tools Appl.
Nihal Kumar, Om Prakash Verma, Anil Singh Parihar
2025 J jnl
Signal Image Video Process.
Divya Yadav, Deepika Rani, Om Prakash Verma
2025 J jnl
Comput. Biol. Medicine
Divya Yadav, Deepika Rani, Om Prakash Verma
2025 J jnl
Signal Image Video Process.
Amit Kumar Dwivedi, Om Prakash Verma, Sachin Taran
2025 J jnl
Multim. Tools Appl.
Poonam Rani, Om Prakash Verma
2025 J jnl
J. Supercomput.
Puneet Kansal, Manoj Kumar, Om Prakash Verma
2025 conf
ICECET
Divya Arora Bhayana, Om Prakash Verma
2025 J jnl
ACM Trans. Multim. Comput. Commun. Appl.
Divya Arora Bhayana, Om Prakash Verma
2024 J jnl
Comput. Electr. Eng.
Anshu Khurana, Om Prakash Verma
2024 J jnl
Multim. Tools Appl.
Tejna Khosla, Om Prakash Verma
2024 J jnl
Signal Image Video Process.
Divya Arora Bhayana, Om Prakash Verma
2023 J jnl
Comput. Syst. Sci. Eng.
K. Sreelakshmy, Himanshu Gupta, Om Prakash Verma, Kapil Kumar, Abdelhamied A. Ateya, Naglaa F. Soliman
2023 J jnl
Appl. Soft Comput.
Himanshu Gupta, Om Prakash Verma
2023 J jnl
Trans. Emerg. Telecommun. Technol.
Samayveer Singh, Mohit Kumar, Om Prakash Verma, Rajeev Kumar, Sukhpal Singh Gill
2023 J jnl
Multim. Tools Appl.
Tejna Khosla, Om Prakash Verma
2023 J jnl
IEEE Access
Tripti Mahara, Helen Josephine V. L, Rashmi Srinivasan, Poorvi Prakash, Abeer D. Algarni, Om Prakash Verma
2023 J jnl
Evol. Intell.
Dawit Kiros Redie, Abdulhakim Edao Sirko, Tensaie Melkamu Demissie, Semagn Sisay Teferi, Vimal K. Shrivastava, Om Prakash Verma, Tarun Kumar Sharma
2023 J jnl
IEEE Trans. Artif. Intell.
Anshu Khurana, Om Prakash Verma
2023 conf
ICCCNT
Skalzang Diskit, Spalzes Angmo, Shabana Tabassum, Om Prakash Verma
2023 J jnl
Multim. Tools Appl.
Saurav Kumar, Drishti Yadav, Himanshu Gupta, Mohit Kumar, Om Prakash Verma
2023 J jnl
Evol. Intell.
Himanshu Gupta, Om Prakash Verma
2022 J jnl
Pers. Ubiquitous Comput.
Nirmal Pandey, Om Prakash Verma, Amioy Kumar
2022 J jnl
J. Supercomput.
Puneet Kansal, Manoj Kumar, Om Prakash Verma
2022 J jnl
Complex Intell. Syst.
Himanshu Gupta, Hirdesh Varshney, Tarun Kumar Sharma, Nikhil Pachauri, Om Prakash Verma
2022 J jnl
Multim. Tools Appl.
Heena Hooda, Om Prakash Verma
2022 J jnl
Multim. Tools Appl.
Himanshu Gupta, Om Prakash Verma
2022 J jnl
Evol. Syst.
Ritu Agarwal, Om Prakash Verma
2022 conf
ECCV Workshops (8)
Matej Kristan, Ales Leonardis, Jirí Matas, Michael Felsberg, Roman P. Pflugfelder, Joni-Kristian Kämäräinen, Hyung Jin Chang, Martin Danelljan, Luka Cehovin Zajc, Alan Lukezic, Ondrej Drbohlav, Johanna Björklund, Yushan Zhang, Zhongqun Zhang, Song Yan, Wenyan Yang, Dingding Cai, Christoph Mayer, Gustavo Fernández, Kang Ben, Goutam Bhat, Hong Chang, Guangqi Chen, Jiaye Chen, Shengyong Chen, Xilin Chen, Xin Chen, Xiuyi Chen, Yiwei Chen, Yu-Hsi Chen, Zhixing Chen, Yangming Cheng, Angelo Ciaramella, Yutao Cui, Benjamin Dzubur, Mohana Murali Dasari, Qili Deng, Debajyoti Dhar, Shangzhe Di, Emanuel Di Nardo, Daniel K. Du, Matteo Dunnhofer, Heng Fan, Zhenhua Feng, Zhihong Fu, Shang Gao, Rama Krishna Sai S. Gorthi, Eric Granger, Q. H. Gu, Himanshu Gupta, Jianfeng He, Keji He, Yan Huang, Deepak Jangid, Rongrong Ji, Cheng Jiang, Yingjie Jiang, Felix Järemo Lawin, Ze Kang, Madhu Kiran, Josef Kittler, Simiao Lai, Xiangyuan Lan, Dongwook Lee, Hyunjeong Lee, Seohyung Lee, Hui Li, Ming Li, Wangkai Li, Xi Li, Xianxian Li, Xiao Li, Zhe Li, Liting Lin, Haibin Ling, Bo Liu, Chang Liu, Si Liu, Huchuan Lu, Rafael M. O. Cruz, Bingpeng Ma, Chao Ma, Jie Ma, Yinchao Ma, Niki Martinel, Alireza Memarmoghadam, Christian Micheloni, Payman Moallem, Le Thanh Nguyen-Meidine, Siyang Pan, Changbeom Park, Danda Pani Paudel, Matthieu Paul, Houwen Peng, Andreas Robinson, Litu Rout, Shiguang Shan, Kristian Simonato, Tianhui Song, Xiaoning Song, Chao Sun, Jingna Sun, Zhangyong Tang, Radu Timofte, Chi-Yi Tsai, Luc Van Gool, Om Prakash Verma, Dong Wang, Fei Wang, Liang Wang, Liangliang Wang, Lijun Wang, Limin Wang, Qiang Wang, Gangshan Wu, Jinlin Wu, Xiaojun Wu, Fei Xie, Tianyang Xu, Wei Xu, Yong Xu, Yuanyou Xu, Wanli Xue, Zizheng Xun, Bin Yan, Dawei Yang, Jinyu Yang, Wankou Yang, Xiaoyun Yang, Yi Yang, Yichun Yang, Zongxin Yang, Botao Ye, Fisher Yu, Hongyuan Yu, Jiaqian Yu, Qianjin Yu, Weichen Yu, Kang Ze, Jiang Zhai, Chengwei Zhang, Chunhu Zhang, Kaihua Zhang, Tianzhu Zhang, Wenkang Zhang, Zhibin Zhang, Zhipeng Zhang, Jie Zhao, Shao-Chuan Zhao, Feng Zheng, Haixia Zheng, Min Zheng, Bineng Zhong, Jiawen Zhu, Xuefeng Zhu, Yueting Zhuang
2022 J jnl
Multim. Tools Appl.
Saurav Kumar, Himanshu Gupta, Drishti Yadav, Irshad Ahmad Ansari, Om Prakash Verma
2021 J jnl
J. Supercomput.
Nisha Chaurasia, Mohit Kumar, Rashmi Chaudhry, Om Prakash Verma
2021 J jnl
Multim. Tools Appl.
Isha Singh, Om Prakash Verma
2021 J jnl
Neural Comput. Appl.
Smitarani Pati, Drishti Yadav, Om Prakash Verma
2020 J jnl
Multim. Tools Appl.
Om Prakash Verma, Heena Hooda
2020 J jnl
Multim. Tools Appl.
Ritu Agarwal, Om Prakash Verma
2020 J jnl
Multim. Tools Appl.
Om Prakash Verma, Nitin Jain, Saibal Kumar Pal
2020 J jnl
Multim. Tools Appl.
Anshu Khurana, Om Prakash Verma
2019 J jnl
Swarm Evol. Comput.
Nirmal Pandey, Om Prakash Verma, Amioy Kumar
2018 J jnl
Multim. Tools Appl.
Rahul Katarya, Om Prakash Verma
2018 J jnl
Sustain. Comput. Informatics Syst.
Om Prakash Verma, Gaurav Manik, Surya Kant, Vinay Kumar Jain, Deepak Kumar Jain, Haoxiang Wang
2018 J jnl
Trans. Inst. Meas. Control
Om Prakash Verma, Toufiq Hazi Mohammed, Shubham Mangal, Gaurav Manik
2018 J jnl
Int. J. Syst. Assur. Eng. Manag.
Om Prakash Verma, Toufiq Haji Mohammed, Shubham Mangal, Gaurav Manik
2018 J jnl
Neural Comput. Appl.
Rahul Katarya, Om Prakash Verma
2018 J jnl
J. Comput. Sci.
Om Prakash Verma, Gaurav Manik, Vinay Kumar Jain
2017 J jnl
IEEE Trans. Fuzzy Syst.
Om Prakash Verma, Anil Singh Parihar
2017 J jnl
Multim. Tools Appl.
Rahul Katarya, Om Prakash Verma
2017 conf
ICSPS
Shamit Lal, Vineet Garg, Om Prakash Verma
2017 conf
ICRAI
Sidharth Raja, Tanuj Bhatia, Akshat Mishra, Sanket Kashyap, Om Prakash Verma
2017 conf
ICSPS
Abhinav Gupta, Om Prakash Verma, Samridh Amla, Siddharth A. Varshney
2017 J jnl
CoRR
Vaibhav Darbari, Saksham Gupta, Om Prakash Verma
2017 J jnl
Swarm Evol. Comput.
Rahul Katarya, Om Prakash Verma
2017 J jnl
IEEE Trans. Image Process.
Anil Singh Parihar, Om Prakash Verma, Chintan Khanna
2017 J jnl
CoRR
Shubham Dokania, Ayush Chopra, Feroz Ahmad, Om Prakash Verma
2017 conf
CSCloud
Rahul Katarya, Om Prakash Verma
2017 J jnl
Int. J. Syst. Assur. Eng. Manag.
Om Prakash Verma, Surya Kant, Gaurav Manik
2016 conf
CVIP (2)
Nitin Sharma, Om Prakash Verma
2016 J jnl
Multim. Tools Appl.
Rahul Katarya, Om Prakash Verma
2016 Misc conf
CISS
Om Prakash Verma, Rishi Raj Chopra, Abhinav Gupta
2016 J jnl
IET Image Process.
Anil Singh Parihar, Om Prakash Verma
2016 J jnl
J. Inf. Secur. Appl.
Vidhi Khanduja, Shampa Chakraverty, Om Prakash Verma
2016 J jnl
Expert Syst. Appl.
Om Prakash Verma, Deepti Aggarwal, Tejna Patodi
2015 conf
NCVPRIPG
Rajni Sethi, Indu Sreedevi, Om Prakash Verma, Veni Jain
2015 conf
SocProS (2)
Om Prakash Verma, Toufiq Haji Mohammed, Shubham Mangal, Gaurav Manik
2015 J jnl
Multim. Tools Appl.
Vidhi Khanduja, Om Prakash Verma, Shampa Chakraverty
2014 conf
AMT
Vidhi Khanduja, Shampa Chakraverty, Om Prakash Verma, Neha Singh
2014 conf
SocProS (2)
Om Prakash Verma, Sonu Kumar, Gaurav Manik
2013 J jnl
J. Inf. Process. Syst.
Om Prakash Verma, Shweta Singh
2013 J jnl
Multidimens. Syst. Signal Process.
Om Prakash Verma, Madasu Hanmandlu, Ashish Kumar Sultania, Anil Singh Parihar
2013 J jnl
Neurocomputing
Madasu Hanmandlu, Om Prakash Verma, Seba Susan, Vamsi Krishna Madasu
2013 J jnl
J. Inf. Process. Syst.
Om Prakash Verma, Munazza Nizam, Musheer Ahmad
2013 J jnl
J. Inf. Process. Syst.
Om Prakash Verma, Veni Jain, Rajni Gumber
2012 J jnl
Int. J. Hybrid Intell. Syst.
Om Prakash Verma, Madasu Hanmandlu, Rishabh Sharma
2012 J jnl
Appl. Soft Comput.
Om Prakash Verma, Puneet Kumar, Madasu Hanmandlu, Sidharth Chhabra
2011 J jnl
Pattern Recognit. Lett.
Om Prakash Verma, Madasu Hanmandlu, Puneet Kumar, Sidharth Chhabra, Akhil Jindal
2010 conf
ACIS-ICIS
Om Prakash Verma, Madasu Hanmandlu, Ashish Kumar Sultania, Dhruv
2010 conf
ITNG
Om Prakash Verma, Madasu Hanmandlu, Vamsi Krishna Madasu, Shantaram Vasikarla
2009 J jnl
IEEE Trans. Instrum. Meas.
Madasu Hanmandlu, Om Prakash Verma, Nukala Krishna Kumar, Muralidhar Kulkarni
2009 conf
ITNG
Madasu Hanmandlu, Om Prakash Verma, Pankaj Gangwar, Shantaram Vasikarla
redb/extractors/decompiler/bninja/decompiler.py
← Index redb/extractors/decompiler/bninja/decompiler.py python
import os
import time
import json

from .analysis.medium_level import MediumLevelAnalysis

# disable the plugins set by user for binary ninja
os.environ["BN_DISABLE_USER_PLUGINS"] = "True"
import traceback

# Binary Ninja imports (conditional)
try:
    import binaryninja
    from binaryninja import mainthread, Symbol
    from binaryninja.enums import SymbolType
    BINARYNINJA_AVAILABLE = True
except Exception:
    BINARYNINJA_AVAILABLE = False
    binaryninja = None
    mainthread = None
    Symbol = None
    SymbolType = None

# Support both package and standalone imports
try:
    # Package import (when imported from redb)
    from .utils.hashes import calculate_sha256, calculate_tlsh
    from .utils.license import set_license
    from .utils.logging import setup_default_logger
    from .analysis.strings import StringAnalysis

    # Only import modules that depend on Binary Ninja when available
    if BINARYNINJA_AVAILABLE:
        from .analysis.cfg import CFGAnalysis
        from .analysis.disassembly import DisassemblyAnalysis
        from .analysis.low_level import LowLevelAnalysis
        from .arch.creator import ArchitectureCreator
        from .custom_options import register_custom_analysis_options
        from .function_type import FunctionTypeAnalysis, FunctionType
        from .analysis.scores import ObfuscationScores

except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.utils.hashes import calculate_sha256, calculate_tlsh
    from redb.extractors.decompiler.bninja.utils.license import set_license
    from redb.extractors.decompiler.bninja.utils.logging import setup_default_logger

    # Only import modules that depend on Binary Ninja when available
    if BINARYNINJA_AVAILABLE:
        from redb.extractors.decompiler.bninja.analysis.cfg import CFGAnalysis
        from redb.extractors.decompiler.bninja.analysis.disassembly import DisassemblyAnalysis
        from redb.extractors.decompiler.bninja.analysis.low_level import LowLevelAnalysis
        from redb.extractors.decompiler.bninja.arch.creator import ArchitectureCreator
        from redb.extractors.decompiler.bninja.custom_options import register_custom_analysis_options
        from redb.extractors.decompiler.bninja.function_type import FunctionTypeAnalysis


class BinaryNinjaDecompiler:
    """A Binary Ninja-based decompiler that replicates the functionality of GhidraDecompilerScript.
    This class extracts decompiled code, disassembly with multiple normalization levels,
    and control flow graph information from binary files.
    """

    MIN_FUNCTION_SIZE = 10  # instructions
    MIN_BLOCK_SIZE = 4  # instructions
    INVALID_STACK_SIZE = -1

    def __init__(
        self,
        filepath,
        timeout,
        log=None,
        exporters=None,
        index_prefix=None,
        filetype=None,
        goresym=None,
        decompile_modules=None,
    ):
        """Initialize the Binary Ninja decompiler.

        Args:
            filepath: Path to the binary file to analyze
            log: Logger object (optional)
            timeout: Maximum time in seconds for analysis (default: 1200)
            exporters: List of exporters for the results (optional)
            index_prefix: Prefix for elastic index (optional)
            filetype: Type of the file (optional)

        """
        self.filepath = filepath
        self.log = log if log else setup_default_logger("BninjaDecompiler")
        self.BNINJA_TIMEOUT = timeout
        self.bv = None
        self.analysis_results = None
        self.errors = []
        self.exporters = exporters
        self.index_prefix = index_prefix
        self.filetype = filetype
        self.goresym = goresym
        self.decompile_modules = decompile_modules or {"all"}

        set_license(binaryninja)

        # Map to track instruction categorization
        mainthread.set_worker_thread_count(3)
        register_custom_analysis_options(binaryninja)

    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.log.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 __enter__(self):
        """Context manager entry point."""
        self.log.info(f"Opening binary file: {self.filepath}")
        binaryninja.BinaryViewType.add_binaryview_initial_analysis_completion_event(
            self.on_analysis_complete
        )

        #self.bv = binaryninja.load(self.filepath, update_analysis=False)
        self.bv = binaryninja.load(self.filepath, update_analysis=True)
        if self.bv is None:
            raise ValueError(f"Failed to open file: {self.filepath}")

        self.log.info("Waiting for analysis to complete...")

        self.log.debug(f"Binja analysis complete: {len(list(self.bv.functions))} functions")

        # set the architecture
        # todo: personalize this for other architectures
        self.arch = ArchitectureCreator("x86").get()

        # apply goresym
        if self.goresym is not None:
            self.__apply_goresym()
        return self

    def on_analysis_complete(self, bv):
        # Request an additional update after analysis is complete to ensure IL generation
        self.bv = bv
        self.bv.update_analysis()

        return

    def __apply_goresym(self):
        file = self.goresym
        data = None
        try:
            data = json.loads(open(file, 'r').read())
        except Exception as e:
            self.log_error(
                "Failed to open file from goresym: ",
                file,
                e,
                "analyze_binary",
            )

        if data is None:
            return

        self.bv.begin_undo_actions()
        if data.get('UserFunctions') is not None:
            user_functions = data['UserFunctions']
            for func in user_functions:
                try:
                    start = int(func['Start'])
                    name = func['FullName']
                    if self.bv.get_function_at(start) is None:
                        self.bv.create_user_function(start)

                    sym = Symbol(SymbolType.FunctionSymbol, start, name, name, name)
                    self.bv.define_user_symbol(sym)
                except Exception as e:
                    self.log.warning(f"Failed to apply GoReSym symbol for UserFunction {func.get('FullName', 'unknown')} at {func.get('Start', 'unknown')}: {e}")

        if data.get('StdFunctions') is not None:
            standard_functions = data['StdFunctions']
            for func in standard_functions:
                try:
                    start = int(func['Start'])
                    name = func['FullName']
                    if self.bv.get_function_at(start) is None:
                        self.bv.create_user_function(start)

                    sym = Symbol(SymbolType.FunctionSymbol, start, name, name, name)
                    self.bv.define_user_symbol(sym)
                except Exception as e:
                    self.log.warning(f"Failed to apply GoReSym symbol for StdFunction {func.get('FullName', 'unknown')} at {func.get('Start', 'unknown')}: {e}")

        self.bv.commit_undo_actions()
        return

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Context manager exit point - clean up resources."""
        if self.bv:
            # Make sure to cancel any pending analysis
            # (if we have no pending analysis, binary ninja will log an error)

            # todo(@nicolo): investigate
            # if hasattr(self.bv, "abort_analysis"):
            #    self.bv.abort_analysis()
            self.bv.file.close()

        self.log.info("Cleanup completed successfully")
        return

    def tag(self):
        """Return the tag for this extractor."""
        return "DECOMPILED"

    def _module_selected(self, module_name):
        """Check if a decompiler sub-module is selected."""
        return "all" in self.decompile_modules or module_name in self.decompile_modules

    def analyze_binary(self):
        """Run Binary Ninja analysis and return results.

        Respects self.decompile_modules to selectively run/skip sub-modules:
        - strings: independent, skipped if not selected
        - decompilation: leaf module, skipped if not selected
        - disassembly: always runs (backbone — provides hash linkage for all others)
        - llil: leaf module, skipped if not selected
        - cfg: leaf module, skipped if not selected

        Only selected modules' results are appended to the results dict for DB insertion.
        Disassembly is always computed for linkage but only inserted when selected.
        """
        try:
            results = {
                "decompiled": [],
                "disassembled": [],
                "cfg": [],
                "llil": [],
                "errors": [],
                "strings": [],
                "mlil": []
            }

            run_all = "all" in self.decompile_modules
            run_strings = run_all or "strings" in self.decompile_modules
            run_decompilation = run_all or "decompilation" in self.decompile_modules
            run_disassembly = run_all or "disassembly" in self.decompile_modules
            run_llil = run_all or "llil" in self.decompile_modules
            run_cfg = run_all or "cfg" in self.decompile_modules

            # Determine if we need the per-function loop at all
            need_per_function = run_decompilation or run_disassembly or run_llil or run_cfg

            #functions_list = list(filter(is_not_ext_lib_function, self.bv.functions))
            functions_list = list(filter(is_lib_or_thunk, self.bv.functions))
            functions_list = list(filter(self.is_too_few_blocks, functions_list))

            # Strings extraction — independent of per-function analysis
            if run_strings:
                results["strings"] = StringAnalysis(self.bv, functions_list).analyze()

            if not need_per_function:
                return results

            for function in functions_list:
                try:
                    # Decompilation (HLIL) — leaf module, skip if not selected
                    hlil_json = self.extract_hlil(function) if run_decompilation else None

                    # Disassembly — always compute (provides hash linkage for others)
                    disass_json = self.extract_disasm(function)

                    # CFG — leaf module, skip if not selected
                    cfg_json = self.extract_cfg(function) if run_cfg else None

                    # LLIL — leaf module, skip if not selected
                    lowlevel_json = self.extract_lowlevel(function) if run_llil else None

                    # we run mlil only if we have cfg
                    mlil_json = self.extract_mediumlevel(function) if run_llil else None

                    # Calculate fuzzy hashes for disassembly using utils.hashes
                    if disass_json:
                        disass_no_addr = disass_json.get("disassembled_function_no_addresses", "")
                        disass_json["tlsh_disassembly"] = calculate_tlsh(disass_no_addr)

                    if hlil_json and disass_json:
                        hlil_json["disassembled_function_hash"] = disass_json[
                            "disassembled_function_hash"
                        ]
                        disass_json["decompiled_function_hash"] = hlil_json[
                            "decompiled_function_hash"
                        ]

                        results["decompiled"].append(hlil_json)
                        if run_disassembly:
                            results["disassembled"].append(disass_json)

                    elif hlil_json:
                        hlil_json["disassembled_function_hash"] = None
                        results["decompiled"].append(hlil_json)
                    elif disass_json:
                        disass_json["decompiled_function_hash"] = None
                        if run_disassembly:
                            results["disassembled"].append(disass_json)

                    # Add bi-directional linkage between LLIL and disassembly with fuzzy hashes
                    # LLIL fuzzy hashes (tlsh_llil) are already calculated in lowlevel_json
                    if lowlevel_json and disass_json:
                        # Add disassembly info to LLIL
                        lowlevel_json["disassembled_function_hash"] = disass_json["disassembled_function_hash"]
                        lowlevel_json["tlsh_disassembly"] = disass_json.get("tlsh_disassembly")

                        # Add LLIL fuzzy hashes to disassembly for easy export access
                        disass_json["tlsh_llil"] = lowlevel_json.get("tlsh_llil")
                        disass_json["minhash"] = lowlevel_json.get("minhash")

                    elif lowlevel_json:
                        lowlevel_json["disassembled_function_hash"] = None
                        lowlevel_json["tlsh_disassembly"] = None
                    elif disass_json:
                        # No LLIL available for this disassembly
                        disass_json["tlsh_llil"] = None
                        disass_json["minhash"] = None

                    if lowlevel_json:
                        results["llil"].append(lowlevel_json)
                        results["mlil"].append(mlil_json)

                    # Add CFG linkage with disassembled_function_hash
                    # Also add cyclomatic_complexity to disass_json for similarity metrics export
                    if cfg_json and disass_json:
                        cfg_json["disassembled_function_hash"] = disass_json["disassembled_function_hash"]
                        disass_json["cyclomatic_complexity"] = cfg_json.get("cyclomatic_complexity")
                        results["cfg"].append(cfg_json)
                    elif cfg_json:
                        cfg_json["disassembled_function_hash"] = None
                        results["cfg"].append(cfg_json)

                    # Ensure cyclomatic_complexity is set even if no cfg_json
                    if disass_json and "cyclomatic_complexity" not in disass_json:
                        disass_json["cyclomatic_complexity"] = None

                except Exception as e:
                    self.log_error(
                        "Failed to process function: ",
                        function.name,
                        function.start,
                        e,
                        "analyze_binary",
                    )

            return results

        except Exception as e:
            self.log.error(f"Error in binary analysis: {str(e)}")
            return None

    def extract_mediumlevel(self, function):
        middle_level = MediumLevelAnalysis(function, self.bv, self.log)
        middle_level_result, errors = middle_level.analyze()

        for error in errors:
            self.errors.append(error)

        return middle_level_result


    def extract_lowlevel(self, function):
        low_level = LowLevelAnalysis(function, self.bv, self.log)
        disassembly_json, errors = low_level.analyze()

        for error in errors:
            self.errors.append(error)

        return disassembly_json

    def extract_cfg(self, function):
        try:
            llil = function.llil if hasattr(function, 'llil') else None
            cfg = CFGAnalysis(function, llil_function=llil).extract_function_cfg()
            return cfg
        except Exception as e:
            self.log_error(
                "Fatal error in CFG extraction",
                function.name,
                function.start,
                e,
                "extract_disassembly",
            )
            return None

    def extract_disasm(self, function):
        try:
            disass_analysis = DisassemblyAnalysis(
                self.arch, function, self.bv, self.log
            )

            # Create disassembly JSON
            disassembly_json, errors = disass_analysis.get_json()

            for error in errors:
                self.errors.append(error)

            return disassembly_json

        except Exception as e:
            self.log_error(
                "Fatal error in disassembly extraction",
                function.name,
                function.start,
                e,
                "extract_disassembly",
            )
            return None

    def extract_hlil(self, function):
        """Extract HLIL from a function."""
        try:
            # Access function.hlil directly - this will either return the HLIL or raise an exception
            # Removed hlil_if_available check as it was causing race conditions
            if function.hlil is None:
                return None

            if len(function.hlil.basic_blocks) == 0:
                return None

            # if function has one basic block, then compare the len of the instructions against minimum of our functions
            if len(function.hlil.basic_blocks) == 1:
                block = function.basic_blocks[0]
                if len(list(block.disassembly_text)) < self.MIN_FUNCTION_SIZE:
                    return None

            # Get decompiled code
            function_prototype = str(function)
            decompiled_code = str(function.hlil)
            if not decompiled_code or decompiled_code.strip() == "":
                self.log.warning(f"Empty decompilation result for {function.name}")
                return None

            callers = []
            for caller_site in function.caller_sites:
                if caller_site.hlil:
                    callers.append(str(caller_site.hlil))

            calls = []
            for call in function.call_sites:
                if call.hlil:
                    calls.append(str(call.hlil))

            analysis_score = ObfuscationScores(function.hlil)
            flattened_score = analysis_score.flattened_score()
            mba_score = analysis_score.MBA_score()

            if decompiled_code:
                # Create json object
                function_json = {
                    "decompiled_function_hash": calculate_sha256(decompiled_code),
                    "decompiled_function": decompiled_code,
                    "decompiled_function_name": function.name,
                    "decompiled_function_prototype": function_prototype,
                    "decompiled_function_address": function.start,
                    "function_type": FunctionTypeAnalysis(function)
                    .get_function_type()
                    .name,
                    "functions_caller": list(callers),
                    "functions_call": list(calls),
                    "flattened_score": flattened_score,
                    "mba_score": mba_score,
                }
                return function_json
            else:
                return None

        except Exception as e:
            self.log.warning(
                f"Failed to get HLIL for {function.name} at {function.start}: {str(e)}"
            )

        return None

    def extract(self) -> bool:
        """Extract and process all analysis results.

        Returns:
            bool: True if extraction was successful, False otherwise

        """
        self.log.info(f"Starting binary analysis on {self.filepath}")
        try:
            results = self.analyze_binary()
            if not results:
                self.log.error("Analysis failed to produce results")
                return False

            self.analysis_results = results
            self.log.info(
                f"Successfully analyzed binary: {len(results['decompiled'])} decompiled functions, "
                f"{len(results['disassembled'])} disassembled functions, "
                #f"{len(results['cfg'])} basic blocks, "
                f"{len(results['errors'])} errors"
            )
            return True

        except Exception as e:
            self.log.error(f"Error in extraction: {str(e)}")
            return False

    def is_too_few_blocks(self, function):
        if function is None:
            return False

        if function.basic_blocks is None:
            return False

        # when binary ninja does not wait for the analysis, it creates function stubs where basic blocks array
        # is not populated yet. Therefore, we disable this heuristic.
        #if len(function.basic_blocks) == 0:
        #    return False

        # if function has one basic block, then compare the len of the instructions against minimum of our functions
        if len(function.basic_blocks) == 1:
            block = function.basic_blocks[0]
            if len(list(block.disassembly_text)) < self.MIN_FUNCTION_SIZE:
                return False

        return True

def is_lib_or_thunk(function):
    function_type = FunctionTypeAnalysis(function).get_function_type()
    return not (function_type == FunctionType.THUNK or function_type == FunctionType.EXTERNAL or function_type == FunctionType.LIBRARY)