James Cussens

91 papers A* 6A 11B 9C 4Journal 33Unranked 22
YearRankTypeTitle / Venue / Authors
2026 J jnl
J. Mach. Learn. Res.
Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno
2023 conf
CLeaR
James Cussens
2022 A ed.
UAI
James Cussens, Kun Zhang
2021 J jnl
Int. J. Approx. Reason.
Milan Studený, James Cussens, Václav Kratochvíl
2020 conf
PGM
Charupriya Sharma, Zhenyu A. Liao, James Cussens, Peter van Beek
2020 J jnl
CoRR
Charupriya Sharma, Zhenyu A. Liao, James Cussens, Peter van Beek
2020 conf
PGM
Milan Studený, James Cussens, Václav Kratochvíl
2020 conf
PGM
James Cussens
2020 conf
PGM
Teny Handhayani, James Cussens
2020 J jnl
CoRR
Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek
2020 A conf
AISTATS
Alvaro Henrique Chaim Correia, James Cussens, Cassio P. de Campos
2019 A* conf
AAAI
Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek
2019 J jnl
CoRR
Teny Handhayani, James Cussens
2019 J jnl
CoRR
Alvaro H. C. Correia, James Cussens, Cassio P. de Campos
2019 C conf
ICMLA
Durdane Kocacoban, James Cussens
2019 J jnl
CoRR
Durdane Kocacoban, James Cussens
2018 J jnl
CoRR
Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek
2018 J jnl
CoRR
James Cussens
2018 conf
PGM
James Cussens
2018 J jnl
Mach. Learn.
James Cussens, Alessandra Russo
2018 J jnl
Int. J. Approx. Reason.
Arjen Hommersom, James Cussens
2017 A* conf
IJCAI
James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett
2017 J jnl
J. Artif. Intell. Res.
James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett
2017 J jnl
Int. J. Approx. Reason.
Nicos Angelopoulos, James Cussens
2017 ch.
Encyclopedia of Machine Learning and Data Mining
James Cussens
2017 B ed.
ILP
James Cussens, Alessandra Russo
2017 J jnl
Artif. Intell.
Mark Bartlett, James Cussens
2017 J jnl
Math. Program.
James Cussens, David Haws, Milan Studený
2017 ed.
ILP (Short Papers)
James Cussens, Alessandra Russo
2017 J jnl
Int. J. Approx. Reason.
Milan Studený, James Cussens
2016 J jnl
CoRR
James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett
2016 J jnl
Stat. Comput.
Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
2016 conf
Probabilistic Graphical Models
Milan Studený, James Cussens
2015 J jnl
CoRR
James Cussens
2015 J jnl
Theory Pract. Log. Program.
James Cussens, Luc De Raedt, Angelika Kimmig, Taisuke Sato
2015 J jnl
Int. J. Approx. Reason.
Waleed Alsanie, James Cussens
2013 A conf
UAI
James Cussens, Mark Bartlett
2013 J jnl
CoRR
James Cussens, Mark Bartlett
2013 J jnl
CoRR
James Cussens
2013 J jnl
CoRR
Nicos Angelopoulos, James Cussens
2013 J jnl
CoRR
James Cussens
2012 J jnl
CoRR
James Cussens
2012 J jnl
CoRR
James Cussens
2012 J jnl
CoRR
Vítor Santos Costa, David Page, Maleeha Qazi, James Cussens
2012 J jnl
Mach. Learn.
James Cussens
2011 A conf
UAI
James Cussens
2011 conf
ILP (Late Breaking Papers)
Waleed Alsanie, James Cussens
2011 B conf
ILP
James Cussens
2011 conf
RTCSA (1)
Mark Bartlett, Iain Bate, James Cussens, Dimitar Kazakov
2010 B conf
ILP
James Cussens
2010 ch.
Encyclopedia of Machine Learning
James Cussens
2010 A conf
ECAI
Mark Bartlett, Iain Bate, James Cussens
2010 C conf
ICMLA
Mark Bartlett, Iain Bate, James Cussens
2010 conf
WCB@ICLP
James Cussens
2009 conf
DC@PKDD/ECML
Malik Tahir Hassan, Asim Karim, Suresh Manandhar, James Cussens
2009 conf
MLG/SRL@ILP
James Cussens
2009 conf
CIBB
Silvia Liverani, James Cussens, Jim Q. Smith
2008 J jnl
Ann. Math. Artif. Intell.
Nicos Angelopoulos, James Cussens
2008 A conf
UAI
James Cussens
2008 ch.
Probabilistic Inductive Logic Programming
Vítor Santos Costa, David Page, James Cussens
2007 conf
Probabilistic, Logical and Relational Learning - A Further Synthesis
James Cussens
2006 B conf
ILP
Barnaby Fisher, James Cussens
2005 A* conf
IJCAI
Nicos Angelopoulos, James Cussens
2005 conf
Probabilistic, Logical and Relational Learning
Nicos Angelopoulos, James Cussens
2005 A* conf
ICML
Nicos Angelopoulos, James Cussens
2004 B conf
ILP
James Cussens
2003 A conf
UAI
Vítor Santos Costa, David Page, Maleeha Qazi, James Cussens
2002 conf
Computational Logic: Logic Programming and Beyond
James Cussens
2001 A conf
UAI
Nicos Angelopoulos, James Cussens
2001 J jnl
Mach. Learn.
James Cussens
2001 conf
INAP (LNCS Volume)
Nicos Angelopoulos, James Cussens
2001 conf
INAP
Nicos Angelopoulos, James Cussens
2001 A conf
AISTATS
James Cussens
2000 conf
CoNLL/LLL
James Cussens, Stephen Pulman
2000 B ed.
ILP
James Cussens, Alan M. Frisch
2000 ed.
ILP Work-in-progress reports
James Cussens, Alan M. Frisch
2000 ed.
Learning Language in Logic
James Cussens, Saso Dzeroski
2000 A conf
UAI
James Cussens
1999 conf
Learning Language in Logic
Saso Dzeroski, James Cussens, Suresh Manandhar
1999 conf
Learning Language in Logic
James Cussens, Stephen G. Pulman
1999 J jnl
Electron. Trans. Artif. Intell.
James Cussens
1999 A conf
UAI
James Cussens
1999 B conf
ILP
James Cussens, Saso Dzeroski, Tomaz Erjavec
1998 B conf
ILP
James Cussens
1997 B conf
ILP
James Cussens
1996 J jnl
Synth.
James Cussens
1995 A* conf
ICML
James Cussens
1993 conf
ECML
James Cussens
1993 A* conf
AAAI
James Cussens, Anthony Hunter, Ashwin Srinivasan
1992 C conf
IPMU
James Cussens, Anthony Hunter
1991 C conf
ECSQARU
James Cussens, Anthony Hunter
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"