Weilong Li

29 papers A* 1Journal 22Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Xin Xu, Weilong Li, Wei Liu, Wenke Huang, Zhixi Yu, Bin Yang, Xiaoying Liao, Kui Jiang
2026 J jnl
IEEE Trans. Dependable Secur. Comput.
Yabo Wang, Weilong Li, Ruizhi Xiao, Jiakun Sun, Shuyuan Jin
2025 J jnl
Comput. Networks
Yabo Wang, Jiakun Sun, Ruizhi Xiao, Weilong Li, Shuyuan Jin
2025 J jnl
CoRR
Yuqing Shao, Yuchen Yang, Rui Yu, Weilong Li, Xu Guo, Huaicheng Yan, Wei Wang, Xiao Sun
2025 J jnl
Multim. Syst.
Weilong Li, Qiang Zhang, Lei Zhang, Xiao-Yuan Jing
2025 J jnl
IEEE Trans. Instrum. Meas.
Juan Li, Yucong Chen, Weilong Li, Xiaotao Liu, Zhiguo Xu, Faming Luo, Zulong Zhao, Xincai Kang, Ruishi Mao, Zhengguo Hu
2025 conf
ICA3PP (6)
Xiaoyong Tang, Weilong Li, Wenzheng Liu, Ronghui Cao, Tan Deng
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Hongwei Zhang, Xiaolin Zhao, Xuan Hao, Weilong Li, Hang Hu, Jiacheng Ni, Ying Luo
2024 J jnl
IEEE Trans. Netw. Serv. Manag.
Ruizhi Xiao, Weilong Li, Jintian Lu, Shuyuan Jin
2024 conf
NLPCC (5)
Ruihua Qi, Weilong Li, Haobo Lyu
2024 A* conf
ASE
Weilong Li, Jintian Lu, Ruizhi Xiao, Pengfei Shao, Shuyuan Jin
2024 J jnl
High Confid. Comput.
Huan Wang, Yunlong Tang, Yan Wang, Ning Wei, Junyi Deng, Zhiyan Bin, Weilong Li
2024 J jnl
IEEE Internet Things J.
Jintian Lu, Weilong Li, Jiakun Sun, Ruizhi Xiao, Bolin Liao
2023 J jnl
IEEE Trans. Netw. Serv. Manag.
Ruizhi Xiao, Hao Chen, Jintian Lu, Weilong Li, Shuyuan Jin
2023 J jnl
Appl. Soft Comput.
Yan Wang, Haifeng Zhang, Yongjun Wei, Huan Wang, Yong Peng, Zhiyan Bin, Weilong Li
2023 J jnl
Remote. Sens.
Quan Yin, Yuting Zhang, Weilong Li, Jianjun Wang, Weiling Wang, Irshad Ahmad, Guisheng Zhou, Zhongyang Huo
2023 J jnl
Remote. Sens.
Weilong Li, Jianjun Wang, Yuting Zhang, Quan Yin, Weiling Wang, Guisheng Zhou, Zhongyang Huo
2023 J jnl
Remote. Sens.
Quan Yin, Yuting Zhang, Weilong Li, Jianjun Wang, Weiling Wang, Irshad Ahmad, Guisheng Zhou, Zhongyang Huo
2022 J jnl
Adv. Multim.
Weilong Li, Jie Li, Junhui Zhou
2021 J jnl
Bioinform.
Jesper Lund, Weilong Li, Afsaneh Mohammadnejad, Shuxia Li, Jan Baumbach, Qihua Tan
2020 conf
ISPA/BDCloud/SocialCom/SustainCom
Weilong Li, Shuyuan Jin
2019 conf
AIM
Ni Cao, Wansheng Jiang, Zheng Pei, Weilong Li, Zhanxi Wang, Zhijie Huo
2018 J jnl
Bioinform.
Weilong Li, Lene Christiansen, Jacob v. B. Hjelmborg, Jan Baumbach, Qihua Tan
2018 conf
BIBM
Afsaneh Mohammadnejad, Shuxia Li, Hongmei Duan, Jesper Lund, Weilong Li, Jan Baumbach, Qihua Tan
2017 J jnl
KSII Trans. Internet Inf. Syst.
Weilong Li, De-Wei Wu, Jia Du, Yang Zhou
2016 J jnl
J. Intell. Robotic Syst.
Yang Zhou, De-Wei Wu, Jia Du, Weilong Li
2016 J jnl
Sci. China Inf. Sci.
Feng Shi, Jianxin Wang, Yufei Yang, Qilong Feng, Weilong Li, Jianer Chen
2015 J jnl
Bioinform.
Junwei Luo, Jianxin Wang, Weilong Li, Zhen Zhang, Fang-Xiang Wu, Min Li, Yi Pan
2013 conf
BIC-TA
Qing Wang, Weilong Li, Lihua Zhang, Sheng Lu
redb/extractors/ioc_extractor/ioc_extractor.py
← Index redb/extractors/ioc_extractor/ioc_extractor.py python
"""
IOC Extractor - Extractor class for extracting IOCs from decompilation results.

This extractor works with in-memory data from DecompileBinja, following the
standard Extractor pattern to support both ClickHouse and PrintExporter (dry-run).

Usage:
    # After DecompileBinja completes:
    ioc_extractor = IOCExtractorFromResults(
        analysis_results=decompiler.analysis_results,
        sha256=sha256,
        log=logger,
        exporters=exporters,
        index_prefix=index_prefix
    )
    ioc_extractor.export_data()
"""

import inspect
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, List, Dict, Optional

from redb.extractors.enum import Tag
from redb.extractors.database_exporters import DatabaseExporter

# Import the IOCScraper and related classes from standalone module
from redb.extractors.ioc_extractor.standalone_ioc_extractor import (
    IOCScraper,
    IOCType,
    SourceType,
    ExtractedIOC,
)
from typing import Set


class IOCExtractorFromResults:
    """
    Extracts IOCs from in-memory decompilation results.

    This follows a simplified Extractor pattern but doesn't inherit from Extractor
    since it doesn't read from a binary file - instead it takes already-processed
    analysis results from DecompileBinja.
    """

    def __init__(
        self,
        analysis_results: Dict[str, Any],
        sha256: str,
        log: Any,
        exporters: Optional[List[DatabaseExporter]] = None,
        index_prefix: Optional[str] = None,
        tld_file: Optional[Path] = None,
        suppress_types: Optional[Set[IOCType]] = None,
        js_context: bool = False,
    ):
        """
        Initialize IOC Extractor with analysis results.

        Args:
            analysis_results: Dict containing 'strings' and 'decompiled' lists from DecompileBinja
            sha256: Sample SHA256 hash
            log: Logger instance
            exporters: List of database exporters (ClickHouse, Print, etc.)
            index_prefix: Index prefix for database
            tld_file: Optional path to TLD list file
            js_context: When True, the underlying IOCScraper rejects FQDN
                candidates that match JS object-access syntax (see
                JS_FP_TLDS / JS_FP_SLDS). Set this for the JS pipeline only;
                APK suppresses FQDN entirely via suppress_types and binary
                callers leave it disabled.
        """
        self.log = log
        self.log.debug(f"Creating {self.__class__.__name__}")
        self.analysis_results = analysis_results
        self.sha256 = sha256
        self.exporters = exporters or []
        self.index_prefix = index_prefix
        self.scraper = IOCScraper(
            tld_file, suppress_types=suppress_types, js_context=js_context,
        )
        self.extracted_iocs: List[ExtractedIOC] = []

    def extract(self) -> List[ExtractedIOC]:
        """
        Extract IOCs from strings and decompiled functions in analysis_results.

        Returns:
            List of ExtractedIOC objects
        """
        self.log.debug(inspect.currentframe().f_code.co_name)
        self.extracted_iocs = []

        # Extract from strings
        strings_count = self._extract_from_strings()

        # Extract from decompiled functions
        functions_count = self._extract_from_decompiled()

        # Extract from text-based artefact surfaces (JS, PowerShell, etc.)
        text_count = self._extract_from_text()

        self.log.info(
            f"Extracted {len(self.extracted_iocs)} IOCs for {self.sha256[:16]}... "
            f"(strings: {strings_count}, functions: {functions_count}, "
            f"text: {text_count})"
        )

        return self.extracted_iocs

    def _extract_from_strings(self) -> int:
        """Extract IOCs from sample's strings."""
        count = 0
        strings = self.analysis_results.get("strings", [])

        for s in strings:
            string_value = s.get("string", "")
            string_offset = s.get("string_offset", 0)

            if isinstance(string_value, bytes):
                string_value = string_value.decode('utf-8', errors='replace')

            for ioc in self.scraper.scrape(string_value, SourceType.STRING, str(string_offset)):
                self.extracted_iocs.append(ioc)
                count += 1

        return count

    def _extract_from_decompiled(self) -> int:
        """Extract IOCs from sample's decompiled functions.

        Supports both Binja format (key: "decompiled", fields: "decompiled_function",
        "decompiled_function_hash", "function_type") and APK format (key:
        "decompiled_content", fields: "decompiled_method", "decompiled_method_hash",
        "method_type").
        """
        count = 0

        # Binja format
        decompiled = self.analysis_results.get("decompiled", [])
        for func in decompiled:
            func_type = func.get("function_type", "UNKNOWN")
            if func_type in ("LIBRARY", "THUNK"):
                continue

            func_content = func.get("decompiled_function", "")
            func_hash = func.get("decompiled_function_hash", "unknown")

            if isinstance(func_content, bytes):
                func_content = func_content.decode('utf-8', errors='replace')

            for ioc in self.scraper.scrape(func_content, SourceType.DECOMPILED_FUNCTION, func_hash):
                self.extracted_iocs.append(ioc)
                count += 1

        # APK format (decompiled_content with method-level fields)
        decompiled_content = self.analysis_results.get("decompiled_content", [])
        for func in decompiled_content:
            func_type = func.get("method_type", "UNKNOWN")
            if func_type in ("LIBRARY", "THUNK"):
                continue

            func_content = func.get("decompiled_method", "")
            func_hash = func.get("decompiled_method_hash", "unknown")

            if isinstance(func_content, bytes):
                func_content = func_content.decode('utf-8', errors='replace')

            for ioc in self.scraper.scrape(func_content, SourceType.DECOMPILED_FUNCTION, func_hash):
                self.extracted_iocs.append(ioc)
                count += 1

        return count

    def _extract_from_text(self) -> int:
        """Extract IOCs from text-based artefact surfaces.

        Walks `analysis_results["text_raw"]` and `analysis_results["text_normalized"]`,
        each a list of `{"content": str, "content_hash": str}` dicts. Each
        list is routed through its own SourceType (`TEXT_RAW` /
        `TEXT_NORMALIZED`) so analysts can distinguish IOCs that were already
        present in the raw source from those exposed only after normalisation
        (deobfuscation/beautification). Generic across text-based formats —
        used by JS today, intended for PowerShell, Python, email body,
        extracted PDF/Office text in the future.
        """
        count = 0

        for key, source_type in (
            ("text_raw", SourceType.TEXT_RAW),
            ("text_normalized", SourceType.TEXT_NORMALIZED),
        ):
            for entry in self.analysis_results.get(key, []):
                content = entry.get("content", "")
                content_hash = entry.get("content_hash", "unknown")

                if isinstance(content, bytes):
                    content = content.decode('utf-8', errors='replace')

                for ioc in self.scraper.scrape(content, source_type, content_hash):
                    self.extracted_iocs.append(ioc)
                    count += 1

        return count

    def prepare_export_data(self, exporter_type: str) -> Any:
        """
        Prepare data for specific export type.

        Returns tuple for ClickHouse or list of dicts for Print/Elasticsearch.
        """
        self.log.debug(inspect.currentframe().f_code.co_name)

        if not self.extracted_iocs:
            return None

        now = datetime.now(timezone.utc)

        if exporter_type == "ClickHouseExporter":
            data = [
                [
                    self.sha256,
                    ioc.ioc_type.value,
                    ioc.ioc_value,
                    ioc.source_type.value,
                    ioc.source_identifier,
                    now,
                ]
                for ioc in self.extracted_iocs
            ]

            column_names = [
                "sha256",
                "ioc_type",
                "ioc_value",
                "source_type",
                "source_identifier",
                "extracted_at",
            ]

            column_type_names = [
                "FixedString(64)",
                "Enum8('ipv4'=1, 'ipv6'=2, 'fqdn'=3, 'url'=4, 'email'=5, 'server'=6, "
                "'hash_md5'=10, 'hash_sha1'=11, 'hash_sha256'=12, 'cve'=20, 'cwe'=21, 'cpe'=22, "
                "'crypto_btc'=30, 'crypto_eth'=31, 'crypto_xrp'=32, 'crypto_bch'=33, "
                "'crypto_ada'=34, 'crypto_substrate'=35, 'path_linux'=40, 'path_windows'=41, "
                "'registry_key'=42, 'onion'=50)",
                "String",
                "Enum8('decompiled_function'=1, 'disassembled_function'=2, 'string'=3, "
                "'text_raw'=4, 'text_normalized'=5)",
                "String",
                "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

        else:
            # For PrintExporter and others - return list of dicts
            return [
                {
                    "sha256": self.sha256,
                    "ioc_type": ioc.ioc_type.value,
                    "ioc_value": ioc.ioc_value,
                    "source_type": ioc.source_type.value,
                    "source_identifier": ioc.source_identifier,
                    "extracted_at": now.isoformat(),
                }
                for ioc in self.extracted_iocs
            ]

    def get_clickhouse_table(self) -> str:
        """Return the ClickHouse table name for IOCs."""
        return "redb_iocs"

    def tag(self) -> str:
        """Return the tag for this extractor."""
        return Tag.IOC.value if hasattr(Tag, 'IOC') else "ioc"

    def export_data(self) -> bool:
        """
        Export extracted IOCs to all configured exporters.

        Returns:
            True if export succeeded, False if failed, None if no data
        """
        self.log.debug(inspect.currentframe().f_code.co_name)

        # First extract the IOCs
        extracted = self.extract()

        if not extracted:
            self.log.debug("No IOCs extracted, skipping export")
            return None

        success = True

        from redb.extractors.database_exporters import PrintExporter, ClickHouseExporter

        for exporter in self.exporters:
            try:
                if isinstance(exporter, PrintExporter):
                    # For PrintExporter, pass the list of dicts
                    export_data = self.prepare_export_data("PrintExporter")
                    success &= exporter.export(export_data)

                elif isinstance(exporter, ClickHouseExporter):
                    # For ClickHouse, pass tuple with table info
                    export_data = self.prepare_export_data("ClickHouseExporter")
                    if export_data:
                        success &= exporter.export(
                            export_data,
                            table=self.get_clickhouse_table(),
                            column_names=export_data[1],
                            column_type_names=export_data[2]
                        )

            except Exception as e:
                self.log.error(f"Error exporting IOCs to {exporter.__class__.__name__}: {e}")
                success = False

        return success