Vignesh Narayanan

78 papers A* 3A 1B 3C 9Journal 44Unranked 18
YearRankTypeTitle / Venue / Authors
2026 J jnl
IEEE Trans. Ind. Informatics
Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan
2026 J jnl
CoRR
Protik Nag, Krishnan Raghavan, Vignesh Narayanan
2026 J jnl
CoRR
Md. Abir Hossen, Mohammad Ali Javidian, Vignesh Narayanan, Jason M. O'Kane, Pooyan Jamshidi
2026 A* conf
AAAI
Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan
2026 J jnl
IEEE Trans. Ind. Informatics
Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan
2025 J jnl
CoRR
Arian Yousefian, Avimanyu Sahoo, Vignesh Narayanan
2025 C conf
ACC
Arian Yousefian, Avimanyu Sahoo, Vignesh Narayanan
2025 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Yuan-Hung Kuan, Vignesh Narayanan, Jr-Shin Li
2025 conf
CDC
Arian Yousefian, Avimanyu Sahoo, Vignesh Narayanan
2025 J jnl
CoRR
Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan
2025 J jnl
CoRR
Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan
2025 J jnl
IEEE Control. Syst. Lett.
Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan
2024 conf
CDC
Arian Yousefian, Avimanyu Sahoo, Vignesh Narayanan
2024 conf
AAAI Spring Symposia
Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit P. Sheth
2024 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan
2024 J jnl
IEEE Trans. Autom. Control.
Vignesh Narayanan, Wei Zhang, Jr-Shin Li
2024 conf
AAAI Spring Symposia
Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit P. Sheth
2024 A* conf
AAAI
Bharath Muppasani, Vignesh Narayanan, Biplav Srivastava, Michael N. Huhns
2024 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Yao-Chi Yu, Vignesh Narayanan, Jr-Shin Li
2024 C conf
ACC
Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
2024 J jnl
CoRR
Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan
2024 J jnl
Sensors
Chathurangi Shyalika, Kaushik Roy, Renjith Prasad, Fadi El Kalach, Yuxin Zi, Priya Mittal, Vignesh Narayanan, Ramy F. Harik, Amit P. Sheth
2024 A* conf
NeurIPS
Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava, Michael N. Huhns
2024 J jnl
CoRR
Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava, Michael N. Huhns
2023 J jnl
CoRR
Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Raghava Mutharaju, Michael N. Huhns, Vignesh Narayanan
2023 B conf
SMC
Revathy Venkataramanan, Kaushik Roy, Kanak Raj, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit P. Sheth
2023 J jnl
CoRR
Revathy Venkataramanan, Kaushik Roy, Kanak Raj, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit P. Sheth
2023 conf
CCTA
Geetika Vennam, Avimanyu Sahoo, Vignesh Narayanan
2023 J jnl
CoRR
Biplav Srivastava, Kausik Lakkaraju, Tarmo Koppel, Vignesh Narayanan, Ashish Kundu, Sachindra Joshi
2023 J jnl
IEEE Trans. Syst. Man Cybern. Syst.
Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan
2023 J jnl
CoRR
Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit P. Sheth
2023 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Wei Miao, Vignesh Narayanan, Jr-Shin Li
2023 J jnl
CoRR
Kaushik Roy, Tarun Garg, Vedant Palit, Yuxin Zi, Vignesh Narayanan, Amit P. Sheth
2023 J jnl
CoRR
Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit P. Sheth
2023 J jnl
AI Mag.
Bharath Muppasani, Vishal Pallagani, Kausik Lakkaraju, Shuge Lei, Biplav Srivastava, Brett W. Robertson, Andrea Hickerson, Vignesh Narayanan
2023 J jnl
CoRR
Kaushik Roy, Yuxin Zi, Manas Gaur, Jinendra Malekar, Qi Zhang, Vignesh Narayanan, Amit P. Sheth
2022 J jnl
IEEE Trans. Cybern.
Vignesh Narayanan, Hamidreza Modares, Sarangapani Jagannathan, Frank L. Lewis
2022 J jnl
CoRR
Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit P. Sheth
2022 J jnl
CoRR
Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit P. Sheth
2022 J jnl
CoRR
Bharath Muppasani, Vishal Pallagani, Kausik Lakkaraju, Shuge Lei, Biplav Srivastava, Brett W. Robertson, Andrea Hickerson, Vignesh Narayanan
2021 J jnl
Knowl. Inf. Syst.
Liang Wang, Vignesh Narayanan, Yao-Chi Yu, Yikyung Park, Jr-Shin Li
2021 C conf
ACC
Avimanyu Sahoo, Vignesh Narayanan, Qiming Zhao
2021 J jnl
CoRR
Raghavan Krishnan, Vignesh Narayanan, Jagannathan Sarangapani
2021 J jnl
CoRR
Wei Miao, Vignesh Narayanan, Jr-Shin Li
2021 J jnl
CoRR
Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani
2021 J jnl
IEEE Trans. Cybern.
Wei-Cheng Jiang, Vignesh Narayanan, Jr-Shin Li
2021 conf
TPS-ISA
Vignesh Narayanan, Brett W. Robertson, Andrea Hickerson, Biplav Srivastava, Bryant Walker Smith
2020 J jnl
Neural Networks
Avimanyu Sahoo, Vignesh Narayanan
2020 conf
SSCI
Avimanyu Sahoo, Vignesh Narayanan, Qiming Zhao
2020 C conf
ACC
Yao-Chi Yu, Vignesh Narayanan, ShiNung Ching, Jr-Shin Li
2019 C conf
ACC
Vignesh Narayanan, Jason T. Ritt, Jr-Shin Li, ShiNung Ching
2019 J jnl
IEEE Trans. Ind. Electron.
Avimanyu Sahoo, Vignesh Narayanan, Sarangapani Jagannathan
2019 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Vignesh Narayanan, Avimanyu Sahoo, Sarangapani Jagannathan, Koshy George
2019 J jnl
J. Comput. Neurosci.
Vignesh Narayanan, Jr-Shin Li, ShiNung Ching
2019 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Vignesh Narayanan, Sarangapani Jagannathan, Kannan Ramkumar
2019 J jnl
Neural Networks
Avimanyu Sahoo, Vignesh Narayanan
2019 conf
CDC
Wei Zhang, Vignesh Narayanan, Jr-Shin Li
2018 conf
SSCI
Vignesh Narayanan, Avimanyu Sahoo, Sarangapani Jagannathan
2018 conf
CDC
Vignesh Narayanan, Avimanyu Sahoo, Sarangapani Jagannathan
2018 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Nathan Szanto, Vignesh Narayanan, Sarangapani Jagannathan
2018 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Vignesh Narayanan, Sarangapani Jagannathan
2018 J jnl
IEEE Trans. Cybern.
Vignesh Narayanan, Sarangapani Jagannathan
2018 conf
CDC
Avimanyu Sahoo, Vignesh Narayanan
2018 conf
SSCI
Avimanyu Sahoo, Vignesh Narayanan, Sarangapani Jagannathan
2018 C conf
ACC
Vignesh Narayanan, Avimanyu Sahoo, Sarangapani Jagannathan
2017 C conf
ACC
Haci Mehmet Guzey, Vignesh Narayanan, Sarangapani Jagannathan, Travis Dierks, Levent Acar
2017 C conf
ACC
Nathan Szanto, Vignesh Narayanan, Sarangapani Jagannathan
2017 B conf
IJCNN
Vignesh Narayanan, Sarangapani Jagannathan
2017 conf
SSCI
Avimanyu Sahoo, Vignesh Narayanan, Sarangapani Jagannathan
2017 conf
SSCI
Avimanyu Sahoo, Vignesh Narayanan, Sarangapani Jagannathan
2016 conf
CDC
Vignesh Narayanan, Sarangapani Jagannathan
2016 C conf
ACC
Vignesh Narayanan, Sarangapani Jagannathan
2016 B conf
IJCNN
Vignesh Narayanan, Sarangapani Jagannathan
2016 conf
CDC
Nathan Szanto, Vignesh Narayanan, Sarangapani Jagannathan
2015 A conf
IROS
Yu Zhang, Vignesh Narayanan, Tathagata Chakraborti, Subbarao Kambhampati
2015 conf
HRI (Extended Abstracts)
Vignesh Narayanan, Yu Zhang, Nathaniel Mendoza, Subbarao Kambhampati
2015 conf
SSCI
Vignesh Narayanan, Sarangapani Jagannathan
2014 J jnl
CoRR
Vignesh Narayanan, Yu Zhang, Nathaniel Mendoza, Subbarao Kambhampati
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"