Osamu Hoshino

53 papers Journal 49Unranked 4
YearRankTypeTitle / Venue / Authors
2022 J jnl
J. Comput. Neurosci.
Osamu Hoshino, Mei Hong Zheng, Yasuhiro Fukuoka
2020 J jnl
J. Comput. Neurosci.
Osamu Hoshino, Rikiya Kameno, Jin Kubo, Kazuo Watanabe
2019 J jnl
Biol. Cybern.
Ryuta Sakamoto, Rikiya Kameno, Taira Kobayashi, Asahi Ishiyama, Kazuo Watanabe, Osamu Hoshino
2019 J jnl
J. Comput. Neurosci.
Osamu Hoshino, Rikiya Kameno, Kazuo Watanabe
2018 J jnl
Neural Comput.
Osamu Hoshino, Mei Hong Zheng, Kazuo Watanabe
2018 J jnl
J. Comput. Neurosci.
Osamu Hoshino, Mei Hong Zheng, Kazuo Watanabe
2017 conf
ISNN (2)
Taira Kobayashi, Asahi Ishiyama, Osamu Hoshino
2016 conf
SCIS&ISIS
Asahi Ishiyama, Hiroakira Matsui, Kazuhiro Tsuboi, Kazuo Watanabe, Osamu Hoshino
2016 J jnl
Neural Comput.
Osamu Hoshino, Mei Hong Zheng, Kazuo Watanabe
2015 J jnl
Biol. Cybern.
Mei Hong Zheng, Kazuo Watanabe, Osamu Hoshino
2015 J jnl
Neural Comput.
Osamu Hoshino
2014 J jnl
Neural Comput.
Osamu Hoshino
2014 J jnl
Neural Comput.
Hiroakira Matsui, Mei Hong Zheng, Osamu Hoshino
2014 J jnl
Neural Comput.
Mei Hong Zheng, Takami Matsuo, Ai Miyamoto, Osamu Hoshino
2013 J jnl
Neural Comput.
Osamu Hoshino
2013 J jnl
Neural Comput.
Osamu Hoshino
2012 J jnl
Neural Comput.
Ai Miyamoto, Jun Hasegawa, Mei Hong Zheng, Osamu Hoshino
2012 J jnl
Cogn. Process.
Ai Miyamoto, Jun Hasegawa, Osamu Hoshino
2012 J jnl
Neural Comput.
Osamu Hoshino
2011 J jnl
Cogn. Process.
Hideyuki Fujiwara, Mei Hong Zheng, Ai Miyamoto, Osamu Hoshino
2011 J jnl
Neural Comput.
Osamu Hoshino
2011 J jnl
Neural Comput.
Osamu Hoshino
2010 J jnl
Neural Comput.
Osamu Hoshino
2010 J jnl
Cogn. Process.
Yusuke Totoki, Takami Matsuo, Mei Hong Zheng, Osamu Hoshino
2009 J jnl
Neural Comput.
Osamu Hoshino
2008 J jnl
Neural Comput.
Osamu Hoshino
2008 conf
ICANN (2)
Yuto Nakamura, Kazuhiro Tsuboi, Osamu Hoshino
2007 J jnl
Neural Comput.
Osamu Hoshino
2007 J jnl
Neural Comput.
Osamu Hoshino
2005 J jnl
Neural Comput.
Osamu Hoshino
2004 J jnl
Neural Comput.
Osamu Hoshino
2003 J jnl
Biol. Cybern.
Osamu Hoshino
2003 J jnl
Biol. Cybern.
Osamu Hoshino, Mei Hong Zheng, Kazuharu Kuroiwa
2002 J jnl
Neurocomputing
Osamu Hoshino, Masayuki Miyamoto, Mei Hong Zheng, Kazuharu Kuroiwa
2002 J jnl
Neurocomputing
Osamu Hoshino, Mei Hong Zheng, Kazuharu Kuroiwa
2002 J jnl
Biol. Cybern.
Osamu Hoshino, Kazuharu Kuroiwa
2002 J jnl
Neural Process. Lett.
Osamu Hoshino
2002 J jnl
Neurocomputing
Osamu Hoshino, Kouji Waki, Mei Hong Zheng, Kazuharu Kuroiwa
2002 J jnl
Connect. Sci.
Osamu Hoshino
2001 J jnl
Neural Comput.
Osamu Hoshino, Satoru Inoue, Yoshiki Kashimori, Takeshi Kambara
2001 J jnl
Neurocomputing
Yoshiki Kashimori, Masanori Minagawa, Satoru Inoue, Osamu Hoshino, Takeshi Kambara
2001 J jnl
Neurocomputing
Osamu Hoshino, Kazuharu Kuroiwa
2001 J jnl
Neurocomputing
Satoru Inoue, Yoshiki Kashimori, Yin Yang, Haoling Liu, Osamu Hoshino, Takeshi Kambara
2000 J jnl
Biol. Cybern.
Tetsuya Oyamada, Yoshiki Kashimori, Osamu Hoshino, Takeshi Kambara
2000 J jnl
Neurocomputing
Satoru Inoue, Manabu Kimyou, Yoshiki Kashimori, Osamu Hoshino, Takeshi Kambara
2000 J jnl
Neurocomputing
Yoshiki Kashimori, Osamu Hoshino, Takeshi Kambara
2000 J jnl
Neurocomputing
Osamu Hoshino, Satoru Inoue, Yoshiki Kashimori, Takeshi Kambara
1999 J jnl
Neurocomputing
Yoshiki Kashimori, Osamu Hoshino, Takeshi Kambara
1999 J jnl
Neurocomputing
Osamu Hoshino, Yoshiki Kashimori, Takeshi Kambara
1999 J jnl
Neurocomputing
Satoru Inoue, Yoshiki Kashimori, Osamu Hoshino, Takeshi Kambara
1998 J jnl
Biol. Cybern.
Osamu Hoshino, Yoshiki Kashimori, Takeshi Kambara
1997 conf
ICNN
Osamu Hoshino, Yoshiki Kashimori, Takeshi Kambara
1997 J jnl
Neural Networks
Osamu Hoshino, Noriaki Usuba, Yoshiki Kashimori, Takeshi Kambara
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"