Ian J. Deary

26 papers Journal 20Unranked 6
YearRankTypeTitle / Venue / Authors
2023 J jnl
NeuroImage
James W. Madole, Colin R. Buchanan, Mijke Rhemtulla, Stuart J. Ritchie, Mark E. Bastin, Ian J. Deary, Simon R. Cox, Elliot M. Tucker-Drob
2020 conf
MIUA
Lucia Ballerini, Ahmed E. Fetit, Stephan Wunderlich, Ruggiero Lovreglio, Sarah McGrory, Maria del Carmen Valdés Hernández, Tom J. MacGillivray, Fergus Doubal, Ian J. Deary, Joanna M. Wardlaw, Emanuele Trucco
2020 J jnl
NeuroImage
Colin R. Buchanan, Mark E. Bastin, Stuart J. Ritchie, David C. Liewald, James W. Madole, Elliot Tucker-Drob, Ian J. Deary, Simon R. Cox
2019 J jnl
Comput. Medical Imaging Graph.
Rafael Ortiz-Ramon, Maria del C. Valdés Hernández, Víctor González-Castro, Stephen D. Makin, Paul A. Armitage, Benjamin S. Aribisala, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw, David Moratal
2019 J jnl
Bioinform.
Joeri J. Meijsen, Alexandros Rammos, Archie Campbell, Caroline Hayward, David J. Porteous, Ian J. Deary, Riccardo E. Marioni, Kristin K. Nicodemus
2018 J jnl
NeuroImage
Clara Alloza, Simon R. Cox, Manuel Blesa Cabez, Paul Redmond, Heather Whalley, Stuart J. Ritchie, Susana Muñoz Maniega, Maria del C. Valdés Hernández, Elliot Tucker-Drob, Stephen M. Lawrie, Joanna M. Wardlaw, Ian J. Deary, Mark E. Bastin
2018 J jnl
J. Imaging
Susana Muñoz Maniega, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw, Jonathan D. Clayden
2018 J jnl
NeuroImage
Gary J. Lewis, David Alexander Dickie, Simon R. Cox, Sherif Karama, Alan C. Evans, John M. Starr, Mark E. Bastin, Joanna M. Wardlaw, Ian J. Deary
2017 J jnl
NeuroImage
Dominic Edward Job, David Alexander Dickie, David Rodriguez Gonzalez, Andrew Robson, Samuel Danso, Cyril R. Pernet, Mark E. Bastin, James P. Boardman, Alison D. Murray, Trevor S. Ahearn, Gordon D. Waiter, Roger T. Staff, Ian J. Deary, Susan D. Shenkin, Joanna M. Wardlaw
2017 J jnl
NeuroImage
Paul Hoffman, Simon R. Cox, Dominika Dykiert, Susana Muñoz Maniega, Maria del C. Valdés Hernández, Mark E. Bastin, Joanna M. Wardlaw, Ian J. Deary
2017 J jnl
NeuroImage
Paul M. Thompson, Ole A. Andreassen, Alejandro Arias-Vasquez, Carrie E. Bearden, Premika S. Boedhoe, Rachel M. Brouwer, Randy L. Buckner, Jan K. Buitelaar, Kazima B. Bulayeva, Dara M. Cannon, Ronald A. Cohen, Patricia J. Conrod, Anders M. Dale, Ian J. Deary, Emily L. Dennis, Marcel A. de Reus, Sylvane Desrivières, Danai Dima, Gary Donohoe, Simon E. Fisher, Jean-Paul Fouche, et al.
2017 conf
MIUA
Susana Muñoz Maniega, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw, Jonathan D. Clayden
2017 conf
FIFI/OMIA@MICCAI
Ahmed E. Fetit, Siyamalan Manivannan, Sarah McGrory, Lucia Ballerini, Alexander S. Doney, Thomas J. MacGillivray, Ian J. Deary, Joanna M. Wardlaw, Fergus Doubal, Gareth J. McKay, Stephen J. McKenna, Emanuele Trucco
2016 J jnl
Comput. Methods Programs Biomed.
Jae-il Kim, Maria del C. Valdés Hernández, Natalie A. Royle, Susana Muñoz Maniega, Benjamin S. Aribisala, Alan J. Gow, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw, Jinah Park
2016 conf
MIUA
Lucia Ballerini, Ruggiero Lovreglio, Maria del C. Valdés Hernández, Víctor González-Castro, Susana Muñoz Maniega, Enrico Pellegrini, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw
2015 J jnl
NeuroImage
Andreas Glatz, Mark E. Bastin, Alexander J. Kiker, Ian J. Deary, Joanna M. Wardlaw, Maria del C. Valdés Hernández
2014 conf
ISBI
Neda Jahanshad, Peter V. Kochunov, Thomas E. Nichols, Emma Sprooten, René C. W. Mandl, Laura Almasy, Rachel M. Brouwer, Joanne E. Curran, Greig I. de Zubicaray, Rali Dimitrova, Peter T. Fox, L. Elliot Hong, Bennett A. Landman, Hervé Lemaître, Lorna M. Lopez, Nicholas G. Martin, Katie L. McMahon, Braxton D. Mitchell, Rene L. Olvera, Charles P. Peterson, Jessika E. Sussmann, Arthur W. Toga, Joanna M. Wardlaw, Margaret J. Wright, Susan N. Wright, Mark E. Bastin, Andrew M. McIntosh, Dorret I. Boomsma, René S. Kahn, Anouk den Braber, Ian J. Deary, Hilleke E. Hulshoff Pol, Douglas Williamson, John Blangero, Dennis van 't Ent, David C. Glahn, Paul M. Thompson
2014 J jnl
NeuroImage
Neda Jahanshad, Peter V. Kochunov, Emma Sprooten, René C. W. Mandl, Thomas E. Nichols, Laura Almasy, John Blangero, Rachel M. Brouwer, Joanne E. Curran, Greig I. de Zubicaray, Ravindranath Duggirala, Peter T. Fox, L. Elliot Hong, Bennett A. Landman, Nicholas G. Martin, Katie L. McMahon, Sarah E. Medland, Braxton D. Mitchell, Rene L. Olvera, Charles P. Peterson, John M. Starr, Jessika E. Sussmann, Arthur W. Toga, Joanna M. Wardlaw, Margaret J. Wright, Hilleke E. Hulshoff Pol, Mark E. Bastin, Andrew M. McIntosh, Ian J. Deary, Paul M. Thompson
2013 J jnl
NeuroImage
Andreas Glatz, Maria del C. Valdés Hernández, Alexander J. Kiker, Mark E. Bastin, Ian J. Deary, Joanna M. Wardlaw
2013 J jnl
NeuroImage
Neda Jahanshad, Peter V. Kochunov, Emma Sprooten, René C. W. Mandl, Thomas E. Nichols, Laura Almasy, John Blangero, Rachel M. Brouwer, Joanne E. Curran, Greig I. de Zubicaray, Ravindranath Duggirala, Peter T. Fox, L. Elliot Hong, Bennett A. Landman, Nicholas G. Martin, Katie McMahon, Sarah E. Medland, Braxton D. Mitchell, Rene L. Olvera, Charles P. Peterson, John M. Starr, Jessika E. Sussmann, Arthur W. Toga, Joanna M. Wardlaw, Margaret J. Wright, Hilleke E. Hulshoff Pol, Mark E. Bastin, Andrew M. McIntosh, Ian J. Deary, Paul M. Thompson
2011 conf
MIUA
Andreas Glatz, Maria del C. Valdés Hernández, Alexander J. Kiker, Mark E. Bastin, Susana Muñoz Maniega, Natalie A. Royle, Ian J. Deary, Joanna M. Wardlaw
2011 J jnl
NeuroImage
Sherif Karama, Roberto Colom, Wendy Johnson, Ian J. Deary, Richard J. Haier, Deborah P. Waber, Claude Lepage, Hooman Ganjavi, Rex E. Jung, Alan C. Evans
2011 J jnl
IEEE Trans. Biomed. Eng.
Moses O. Sokunbi, Roger T. Staff, Gordon D. Waiter, Trevor S. Ahearn, Helen C. Fox, Ian J. Deary, John M. Starr, Lawrence J. Whalley, Alison D. Murray
2008 J jnl
NeuroImage
Gordon D. Waiter, Helen C. Fox, Alison D. Murray, John M. Starr, Roger T. Staff, Victoria J. Bourne, Lawrence J. Whalley, Ian J. Deary
2006 J jnl
NeuroImage
Roger T. Staff, Alison D. Murray, Ian J. Deary, Lawrence J. Whalley
2004 J jnl
NeuroImage
Ian J. Deary, Enrico Simonotto, Martin Meyer, Alan Marshall, Ian Marshall, Nigel H. Goddard, Joanna M. Wardlaw
redb/extractors/macho_extractors/macho_segments.py
← Index redb/extractors/macho_extractors/macho_segments.py python
import hashlib
import inspect
import base64
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.macho_extractor import MachOExtractor
from redb.models.dataclasses import MachOSegment


class MachOSegmentExtractor(MachOExtractor):

    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_segments"
        self.log.debug(inspect.currentframe().f_code.co_name)

    def _is_empty_result(self, extracted_data) -> bool:
        """
        Override: Empty segments is an ERROR, not a valid empty case.
        A valid MachO file must have segments (at minimum __PAGEZERO, __TEXT).
        """
        # Always return False - empty segments should be treated as an error
        return False

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

    def _extract_segments_for_arch(self, arch_name):
        """Extract segment information for a specific architecture."""
        self.log.debug(f"Extracting segments for architecture: {arch_name}")
        segments = []

        try:
            # Get segments using new API with architecture parameter
            segments_data = self.macho.get_segments(arch=arch_name)
            if not segments_data:
                return segments

            # Extract segments for this architecture
            for segment in segments_data:
                try:
                    segment_name = segment.get('segname', 'Unknown')

                    # Calculate segment hash
                    segment_data = self._get_segment_data(segment)
                    if segment_data:
                        seg_sha256 = hashlib.sha256(segment_data).hexdigest()
                    else:
                        seg_sha256 = ""

                    # Use entropy already calculated by machofile module, rounded to 3 decimal places
                    seg_entropy = round(segment.get('entropy', 0.0), 3)

                    # Create segment dataclass with architecture info
                    macho_segment = MachOSegment(
                        segment_name=segment_name,
                        segment_vaddr=segment.get('vaddr', 0),
                        segment_vsize=segment.get('vsize', 0),
                        segment_offset=segment.get('offset', 0),
                        segment_size=segment.get('size', 0),
                        segment_max_vm_protection=segment.get('max_vm_protection', 0),
                        segment_initial_vm_protection=segment.get('initial_vm_protection', 0),
                        segment_nsects=segment.get('nsects', 0),
                        segment_flags=segment.get('flags', 0),
                        segment_entropy=seg_entropy,
                        segment_sha256=seg_sha256,
                    )
                    # Add architecture info to the segment
                    macho_segment.architecture = arch_name
                    segments.append(macho_segment)

                except Exception as e:
                    self.log.warning(
                        f'Unable to process segment "{segment.get("segname", "Unknown")}" for architecture {arch_name} in {self.hash.sha256}: {e}'
                    )
                    continue

            return segments

        except Exception as e:
            self.log.error(f"Error extracting MachO segments for architecture {arch_name}: {e}")
            return segments

    def _extract_segments(self):
        """Extract segment information from all architectures in the MachO binary."""
        self.log.debug(inspect.currentframe().f_code.co_name)
        segments = []

        if not self.macho:
            return segments

        try:
            # Get architectures using new API (already parsed in base class)
            architectures = self.macho.get_architectures()
            if not architectures:
                return segments

            # Process each architecture
            for arch_name in architectures:
                arch_segments = self._extract_segments_for_arch(arch_name)
                segments.extend(arch_segments)

            return segments

        except Exception as e:
            self.log.error(f"Error extracting MachO segments: {e}")
            return segments

    def _get_segment_data(self, segment):
        """Get the raw data for a segment."""
        try:
            offset = segment.get('offset', 0)
            size = segment.get('size', 0)
            
            if size == 0:
                return None
            
            # Read segment data from file
            with open(self.filepath, 'rb') as f:
                f.seek(offset)
                return f.read(size)
                
        except Exception as e:
            self.log.warning(f"Error reading segment data: {e}")
            return None

    def extract(self):
        self.log.debug(inspect.currentframe().f_code.co_name)
        try:
            segments = self._extract_segments()
            return segments
        except Exception as e:
            self.log.error(f"Error extracting MachO segments: {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

            # Get architectures (macho is already parsed in base class)
            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)

            # Loop through each architecture (1 for single, multiple for FAT)
            for arch_name in architectures:
                # Extract segments for this specific architecture
                segments = self._extract_segments_for_arch(arch_name)
                if not segments:
                    continue

                # Get architecture-specific sha256 and header info
                try:
                    arch_general_info = self.macho.get_general_info(arch=arch_name)
                    arch_sha256 = arch_general_info.get('SHA256', self.sha256)

                    # Get raw architecture value
                    arch_header_raw = self.macho.get_macho_header(arch=arch_name)
                    arch_cputype_raw = arch_header_raw.get('cputype', 0) if arch_header_raw else 0
                except Exception as e:
                    self.log.warning(f"Could not get arch-specific data for {arch_name}: {e}")
                    arch_sha256 = self.sha256
                    arch_cputype_raw = 0

                for segment in segments:
                    data.append([
                        arch_sha256,                          # sha256 (architecture-specific)
                        segment.segment_name,                 # segment_name
                        segment.segment_vaddr,                # segment_vaddr
                        segment.segment_vsize,                # segment_vsize
                        segment.segment_offset,               # segment_offset
                        segment.segment_size,                 # segment_size
                        segment.segment_max_vm_protection,    # segment_max_vm_protection
                        segment.segment_initial_vm_protection, # segment_initial_vm_protection
                        segment.segment_nsects,               # segment_nsects
                        segment.segment_flags,                # segment_flags
                        segment.segment_entropy,              # segment_entropy
                        segment.segment_sha256,               # segment_sha256
                        current_time,                         # analysis_date
                    ])

            column_names = [
                'sha256',
                'segment_name', 'segment_vaddr', 'segment_vsize', 'segment_offset',
                'segment_size', 'segment_max_vm_protection', 'segment_initial_vm_protection',
                'segment_nsects', 'segment_flags', 'segment_entropy', 'segment_sha256',
                'analysis_date'
            ]

            if not data:
                return None

            column_type_names = [
                'FixedString(64)',
                'String', 'UInt64', 'UInt64', 'UInt64',
                'UInt64', 'UInt32', 'UInt32',
                'UInt32', 'UInt32', 'Float64', 'FixedString(64)',
                'DateTime64(3, \'UTC\')'
            ]

            return (data, column_names, column_type_names)

        return None

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