Nan Meng

49 papers A* 1B 3Misc 1Journal 35Unranked 9
YearRankTypeTitle / Venue / Authors
2026 J jnl
Expert Syst. Appl.
Ebenezer Nanor, Bernard Mawuli Cobbinah, Qinli Yang, Junming Shao, Nan Meng, Jason Cheung, Philip K. Adjei, Leo Wang
2026 J jnl
CoRR
Ning Bian, Zhong-Feng Sun, Yun-Bin Zhao, Jin-Chuan Zhou, Nan Meng
2026 J jnl
BMC Medical Imaging
Wei Wei, Qianqian Chen, Nan Meng, Xinyu Wang, Yue Liu, Jingwen Zhang, Yaping Wu, Jiayin Pan, Zhun Huang, Yang Yang, Zhe Wang, Qiuyu Liu, Fangfang Fu, Meiyun Wang
2025 J jnl
CoRR
Yuxin Wei, Yue Zhang, Moxin Zhao, Chang Shi, Jason Pui Yin Cheung, Teng Zhang, Nan Meng
2025 J jnl
CoRR
Chang Shi, Nan Meng, Yipeng Zhuang, Moxin Zhao, Jason Pui Yin Cheung, Hua Huang, Xiuyuan Chen, Cong Nie, Wenting Zhong, Guiqiang Jiang, Yuxin Wei, Jacob Hong Man Yu, Si Chen, Xiaowen Ou, Teng Zhang
2025 J jnl
IEEE J. Biomed. Health Informatics
Chang Shi, Nan Meng, Yipeng Zhuang, Jason Pui Yin Cheung, Moxin Zhao, Hua Huang, Xiuyuan Chen, Cong Nie, Wenting Zhong, Guiqiang Jiang, Yuxin Wei, Jacob Hong Man Yu, Si Chen, Xiaowen Ou, Teng Zhang
2025 conf
CIVEMSA
Nan Meng, Weichen Qi, Teng Zhang
2025 J jnl
CoRR
Nan Meng, Yun-Bin Zhao
2025 J jnl
CAAI Trans. Intell. Technol.
Xuan Yu, Yaping Wu, Yan Bai, Nan Meng, Shuting Jin, Qingxia Wu, Lijuan Chen, Ningli Wang, Xiaosheng Song, Guofeng Shen, Meiyun Wang
2025 J jnl
IEEE J. Biomed. Health Informatics
Moxin Zhao, Nan Meng, Jason Pui Yin Cheung, Chris Yuk Kwan Tang, Chenxi Yu, Wenting Zhong, Pengyu Lu, Chang Shi, Yipeng Zhuang, Teng Zhang
2025 J jnl
CoRR
Moxin Zhao, Nan Meng, Jason Pui Yin Cheung, Chris Yuk Kwan Tang, Chenxi Yu, Wenting Zhong, Pengyu Lu, Chang Shi, Yipeng Zhuang, Teng Zhang
2025 conf
CIVEMSA
Tao Huang, Xihe Kuang, Nan Meng, Teng Zhang
2025 J jnl
Neurocomputing
Yingfeng Wang, Muyu Li, Nan Meng, Min Xu
2024 J jnl
ISPRS Int. J. Geo Inf.
Xiaowen Chen, Naiang Wang, Simin Peng, Nan Meng, Haoyun Lv
2024 conf
EMBC
Nan Meng, Jason Pui Yin Cheung, Tao Huang, Moxin Zhao, Yue Zhang, Chenxi Yu, Chang Shi, Teng Zhang
2024 J jnl
CoRR
Nan Meng, Jason Pui Yin Cheung, Tao Huang, Moxin Zhao, Yue Zhang, Chenxi Yu, Chang Shi, Teng Zhang
2024 J jnl
BMC Medical Imaging
Han Jiang, Ziqiang Li, Nan Meng, Yu Luo, Pengyang Feng, Fangfang Fu, Yang Yang, Jianmin Yuan, Zhe Wang, Meiyun Wang
2024 conf
EMBC
Yue Zhang, Nan Meng, Moxin Zhao, Teng Zhang
2024 J jnl
IEEE Trans. Circuits Syst. Video Technol.
Shansi Zhang, Nan Meng, Edmund Y. Lam
2023 J jnl
J. Comput. Appl. Math.
Zhong-Feng Sun, Jin-Chuan Zhou, Yun-Bin Zhao, Nan Meng
2023 J jnl
IEEE Trans. Image Process.
Shansi Zhang, Nan Meng, Edmund Y. Lam
2023 conf
ISBI
Moxin Zhao, Nan Meng, Jason Pui Yin Cheung, Teng Zhang
2023 J jnl
CoRR
Shansi Zhang, Nan Meng, Edmund Y. Lam
2022 J jnl
CoRR
Zhong-Feng Sun, Jin-Chuan Zhou, Yun-Bin Zhao, Nan Meng
2022 J jnl
CoRR
Shansi Zhang, Nan Meng, Edmund Y. Lam
2022 J jnl
J. Glob. Optim.
Nan Meng, Yun-Bin Zhao, Michal Kocvara, Zhong-Feng Sun
2021 J jnl
Remote. Sens.
Zhenmin Niu, Nai-Ang Wang, Nan Meng, Jiang Liu, Xueran Liang, Hongyi Cheng, Penghui Wen, Xinran Yu, Wenjia Zhang, Xiaoyan Liang
2021 J jnl
Remote. Sens.
Yanzheng Yang, Ning Qi, Jun Zhao, Nan Meng, Zijian Lu, Xuezhi Wang, Le Kang, Boheng Wang, Ruonan Li, Jinfeng Ma, Hua Zheng
2021 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Nan Meng, Hayden K. H. So, Xing Sun, Edmund Y. Lam
2021 J jnl
IEEE Trans. Image Process.
Nan Meng, Kai Li, Jianzhuang Liu, Edmund Y. Lam
2020 A* conf
AAAI
Nan Meng, Xiaofei Wu, Jianzhuang Liu, Edmund Y. Lam
2020 J jnl
CoRR
Nan Meng, Xiaofei Wu, Jianzhuang Liu, Edmund Y. Lam
2020 J jnl
CoRR
Nan Meng, Kai Li, Jianzhuang Liu, Edmund Y. Lam
2020 J jnl
IEEE Access
Nan Meng, Zhou Ge, Tianjiao Zeng, Edmund Y. Lam
2020 J jnl
IEEE Trans. Signal Process.
Nan Meng, Yun-Bin Zhao
2019 conf
MICCAI (4)
Nan Meng, Yan Yang, Zongben Xu, Jian Sun
2019 J jnl
IEEE Access
Nan Meng, Xing Sun, Hayden Kwok-Hay So, Edmund Y. Lam
2019 J jnl
CoRR
Nan Meng, Hayden Kwok-Hay So, Xing Sun, Edmund Y. Lam
2019 J jnl
IEEE J. Biomed. Health Informatics
Nan Meng, Edmund Y. Lam, Kevin K. Tsia, Hayden Kwok-Hay So
2019 B conf
ICIP
Nan Meng, Tianjiao Zeng, Edmund Y. Lam
2017 Misc conf
MVA
Nan Meng, Hayden Kwok-Hay So, Edmund Y. Lam
2017 conf
ICSAI
Fangfang Dai, Yue Shi, Nan Meng, Liang Wei, Zhiguo Ye
2016 B conf
IJCNN
Xing Sun, Zhimin Xu, Nan Meng, Edmund Y. Lam, Hayden Kwok-Hay So
2016 J jnl
BMC Bioinform.
Nan Meng, Raghu Machiraju, Kun Huang
2016 B conf
IJCNN
Xing Sun, Nan Meng, Zhimin Xu, Edmund Y. Lam, Hayden Kwok-Hay So
2015 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Nan Meng, Raghu Machiraju, Kun Huang
2013 J jnl
Int. J. Space Based Situated Comput.
Nan Meng, Jiahong Wang, Eiichiro Kodama, Toyoo Takata
2011 conf
BWCCA
Nan Meng, Jiahong Wang, Eiichiro Kodama, Toyoo Takata
2009 conf
HEALTHINF
Mingrui Zhang, Scott Olson, Joan M. Francioni, Tim Gegg-Harrison, Nan Meng, Zhifu Sun, Ping Yang
redb/extractors/macho_extractors/macho_similarity_hashes.py
← Index redb/extractors/macho_extractors/macho_similarity_hashes.py python
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.macho_extractor import MachOExtractor


class MachOSimilarityHashExtractor(MachOExtractor):
    """Extract Mach-O similarity hashes using machofile API.

    Similarity hashes are MD5 fingerprints of sorted, deduplicated binary components:
    - dylib_hash: MD5 of dynamic library names
    - import_hash: MD5 of imported function names
    - export_hash: MD5 of exported symbol names
    - entitlement_hash: MD5 of entitlement names and array values
    - symhash: MD5 of external undefined symbols

    For FAT binaries:
    - Inserts one row per architecture slice with per-slice hashes
    - Inserts one row for the FAT container with combined hashes

    For single-arch binaries:
    - Inserts one row with that architecture's hashes

    Note: parent_sha256 and architecture relationships are tracked in redb_basic_properties,
    not duplicated here. Use JOIN with redb_basic_properties when needed.
    """

    def __init__(
        self,
        filepath,
        log,
        exporters=None,
        index_prefix=None,
        elastic_index=None,
        known_benign=False,
        known_malicious=False,
        macho=None,
    ):
        super().__init__(
            filepath,
            log,
            exporters,
            index_prefix,
            elastic_index,
            known_benign,
            known_malicious,
            macho,
        )
        self.elastic_index = self.index_prefix + "-macho_hashes"
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.MACHO_HASHES.value

    def _extract_similarity_hashes(self, arch_name=None):
        """Extract similarity hashes for a specific architecture."""
        self.log.debug(inspect.currentframe().f_code.co_name)

        if not self.macho:
            return None

        try:
            similarity_hashes = self.macho.get_similarity_hashes(arch=arch_name)
            return similarity_hashes if similarity_hashes else None
        except Exception as e:
            self.log.error(f"Error extracting similarity hashes for arch {arch_name}: {e}")
            return None

    def extract(self):
        self.log.debug(inspect.currentframe().f_code.co_name)
        try:
            if not self.macho:
                return None

            architectures = self.macho.get_architectures()
            if not architectures:
                return None

            if len(architectures) > 1:
                # FAT binary - return combined hashes + per-arch hashes
                results = []

                # First add combined hashes for the FAT container
                all_hashes = self.macho.get_similarity_hashes()
                combined_hashes = all_hashes.get('combined', {}) if all_hashes else {}
                if combined_hashes:
                    combined_hashes['arch_identifier'] = 'fat'
                    results.append(combined_hashes)

                # Then add per-arch hashes
                for arch_name in architectures:
                    hashes = self._extract_similarity_hashes(arch_name)
                    if hashes:
                        hashes['arch_identifier'] = arch_name
                        results.append(hashes)
                return results
            else:
                # Single architecture - return single result
                return self._extract_similarity_hashes(architectures[0])
        except Exception as e:
            self.log.error(f"Error extracting similarity hashes: {e}")
            return None

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ElasticsearchExporter":
            return self.extract()
        elif exporter_type == "ClickHouseExporter":
            if not self.macho:
                return None

            try:
                architectures = self.macho.get_architectures()
                is_fat = len(architectures) > 1
            except Exception as e:
                self.log.error(f"Could not get architectures: {e}")
                return None

            data = []
            current_time = datetime.now(timezone.utc)

            # For FAT binaries, first insert a row for the container with combined hashes
            if is_fat:
                all_hashes = self.macho.get_similarity_hashes()  # Without arch returns all including 'combined'
                combined_hashes = all_hashes.get('combined', {}) if all_hashes else {}
                if combined_hashes:
                    data.append([
                        self.sha256,                                    # sha256 (FAT container)
                        combined_hashes.get('dylib_hash'),              # dylib_hash
                        combined_hashes.get('import_hash'),             # import_hash
                        combined_hashes.get('export_hash'),             # export_hash
                        combined_hashes.get('entitlement_hash'),        # entitlement_hash
                        combined_hashes.get('symhash'),                 # symhash
                        current_time,                                   # analysis_date
                    ])

            # Insert rows for each architecture slice
            for arch_name in architectures:
                # Get architecture-specific sha256
                try:
                    arch_general_info = self.macho.get_general_info(arch=arch_name)
                    arch_sha256 = arch_general_info.get('SHA256', self.sha256)
                except Exception as e:
                    self.log.warning(f"Could not get arch-specific sha256 for {arch_name}: {e}")
                    arch_sha256 = self.sha256

                # Get similarity hashes for this architecture
                similarity_hashes = self._extract_similarity_hashes(arch_name)
                if not similarity_hashes:
                    continue

                data.append([
                    arch_sha256,                                    # sha256 (arch-specific)
                    similarity_hashes.get('dylib_hash'),            # dylib_hash
                    similarity_hashes.get('import_hash'),           # import_hash
                    similarity_hashes.get('export_hash'),           # export_hash
                    similarity_hashes.get('entitlement_hash'),      # entitlement_hash
                    similarity_hashes.get('symhash'),               # symhash
                    current_time,                                   # analysis_date
                ])

            if not data:
                return None

            column_names = [
                'sha256',
                'macho_dylib_hash', 'macho_import_hash', 'macho_export_hash',
                'macho_entitlement_hash', 'macho_symhash',
                'analysis_date'
            ]

            column_type_names = [
                'FixedString(64)',
                'Nullable(FixedString(32))', 'Nullable(FixedString(32))', 'Nullable(FixedString(32))',
                'Nullable(FixedString(32))', 'Nullable(FixedString(32))',
                'DateTime64(3, \'UTC\')'
            ]

            return (data, column_names, column_type_names)

        return None

    def get_clickhouse_table(self) -> str:
        return "redb_hashes"