Celso Pascoli Bottura

45 papers B 9C 8Journal 8Unranked 20
YearRankTypeTitle / Venue / Authors
2022 J jnl
Autom.
Elvis Omar Jara Alegria, Mateus Giesbrecht, Celso Pascoli Bottura
2021 J jnl
Autom.
Elvis Omar Jara Alegria, Mateus Giesbrecht, Celso Pascoli Bottura
2019 C conf
ACC
Elvis Omar Jara Alegria, Celso Pascoli Bottura
2018 conf
ICINCO (1)
Jorge A. Puerto Acosta, Celso Pascoli Bottura
2018 conf
ICINCO (1)
Angie Forero, Celso Pascoli Bottura
2018 conf
ICINCO (1)
Mateus Giesbrecht, Gilmar Barreto, Celso Pascoli Bottura
2017 conf
CEEC
Jorge A. Puerto Acosta, Celso Pascoli Bottura
2017 J jnl
Expert Syst. Appl.
Mateus Giesbrecht, Celso Pascoli Bottura
2017 conf
ICAC
Elvis Omar Jara Alegria, Celso Pascoli Bottura
2016 B conf
CEC
Celso Pascoli Bottura, Mateus Giesbrecht
2016 C conf
CCA
Elvis Omar Jara Alegria, Celso Pascoli Bottura
2015 conf
ICINCO (1)
Elvis Omar Jara Alegria, Hugo Tanzarella Teixeira, Celso Pascoli Bottura
2015 J jnl
Int. J. Nat. Comput. Res.
Mateus Giesbrecht, Celso Pascoli Bottura
2015 conf
ICINCO (1)
Elvis Omar Jara Alegria, Hugo Tanzarella Teixeira, Celso Pascoli Bottura
2015 conf
ICINCO (1)
Hugo Tanzarella Teixeira, Celso Pascoli Bottura
2010 B conf
IEEE Congress on Evolutionary Computation
Mateus Giesbrecht, Celso Pascoli Bottura
2009 J jnl
Fuzzy Sets Syst.
Ginalber L. O. Serra, Celso Pascoli Bottura
2007 conf
ISIC
Eliezer Arantes da Costa, Celso Pascoli Bottura
2007 J jnl
IEEE Trans. Fuzzy Syst.
Ginalber L. O. Serra, Celso Pascoli Bottura
2007 conf
ICINCO-SPSMC
Eliezer Arantes da Costa, Celso Pascoli Bottura
2006 B conf
IJCNN
Erick Vile Grinits, Celso Pascoli Bottura
2006 J jnl
Int. J. Model. Identif. Control.
Ginalber Luiz de Oliveira Serra, Celso Pascoli Bottura
2006 J jnl
Eng. Appl. Artif. Intell.
Ginalber L. O. Serra, Celso Pascoli Bottura
2006 conf
ISDA (1)
Ginalber L. O. Serra, Celso Pascoli Bottura
2005 B conf
SMC
Ginalber L. O. Serra, Celso Pascoli Bottura
2005 C conf
ICINCO
Rogério Bastos Quirino, Celso Pascoli Bottura
2005 B conf
SMC
Annabell Del Real Tamariz, Celso Pascoli Bottura
2005 B conf
IJCNN
Annabell Del Real Tamariz, Celso Pascoli Bottura
2005 B conf
FUZZ-IEEE
Ginalber L. O. Serra, Celso Pascoli Bottura
2005 conf
ISIC
Annabell Del Real Tamariz, Celso Pascoli Bottura, Gilmar Barreto
2004 conf
CDC
Celso Pascoli Bottura, Ginalber Luiz de Oliveira Serra
2004 conf
ISIC
Erick Vile Grinits, Celso Pascoli Bottura
2004 conf
ISIC
Celso Pascoli Bottura, Ginalber Luiz de Oliveira Serra
2003 C conf
ACC
João Viana da Fonseca Neto, Celso Pascoli Bottura
2003 conf
ISIC
Erick Vile Grinits, Celso Pascoli Bottura
2002 C conf
ACC
Celso Pascoli Bottura, Angel F. Torrico Ceceres
2002 conf
CDC
Celso Pascoli Bottura, A. F. Torrico Caceres
2002 C conf
ACC
Celso Pascoli Bottura, Gilmar Barreto, Maurício José Bordon, Annabell Del Real Tamariz
2000 C conf
ACC
Celso Pascoli Bottura, Gilmar Barreto, Maurício José Bordon, Annabell Del Real Tamariz
2000 C conf
ACC
Celso Pascoli Bottura, João Viana da Fonseca Neto
1999 conf
PP
Celso Pascoli Bottura, Gilmar Barreto, Maurício José Bordon, Annabell Del Real Tamariz
1999 conf
PP
Annabell Del Real Tamariz, Celso Pascoli Bottura, João Viana da Fonseca Neto, Gilmar Barreto
1999 B conf
CEC
João Viana da Fonseca Neto, Celso Pascoli Bottura
1999 B conf
IJCNN
Maurício José Bordon, Celso Pascoli Bottura, Marcelo Carvalho Minhoto Teixeira
1997 conf
PP
Celso Pascoli Bottura, Gilmar Barreto, Maurício José Bordon, José Tarcisio Costa Filho
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"