Haibo Bao

49 papers Journal 46Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Appl. Math. Comput.
Mengchen Wang, Haibo Bao
2026 J jnl
Comput. Electron. Agric.
Duan Li, Zixuan Tang, Ruidong Yan, Wenbin Tian, Haibo Bao
2026 J jnl
Signal Process.
Zheng Liu, Haibo Bao
2025 J jnl
J. Frankl. Inst.
Mengchen Wang, Haibo Bao, Jinde Cao
2025 J jnl
IEEE Trans. Fuzzy Syst.
Lirong Liu, Haibo Bao, Jinde Cao
2025 J jnl
IEEE Trans Autom. Sci. Eng.
Cheng Luo, Haibo Bao, Jinde Cao, Zhao-Dong Xu
2025 J jnl
J. Frankl. Inst.
Cheng Luo, Haibo Bao, Jinde Cao
2024 J jnl
J. Frankl. Inst.
Lirong Liu, Haibo Bao, Jinde Cao
2024 J jnl
IEEE Trans. Syst. Man Cybern. Syst.
Xiaonan Liu, Yonggui Kao, Haibo Bao, Ju H. Park
2024 J jnl
Appl. Math. Comput.
Zhuoyuan Huang, Haibo Bao
2023 J jnl
IEEE Trans. Cybern.
Lirong Liu, Mingli Lei, Haibo Bao
2023 J jnl
J. Frankl. Inst.
Xifen Wu, Haibo Bao, Jinde Cao
2023 J jnl
Appl. Math. Comput.
Xifen Wu, Haibo Bao
2022 J jnl
Neurocomputing
Lirong Liu, Haibo Bao
2022 J jnl
Appl. Math. Comput.
Hailian Tan, Jianwei Wu, Haibo Bao
2022 J jnl
Appl. Math. Comput.
Sha Zhu, Haibo Bao
2022 J jnl
Appl. Math. Comput.
Yuan Chen, Jianwei Wu, Haibo Bao
2022 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Yue Cao, Yonggui Kao, Ju H. Park, Haibo Bao
2022 J jnl
Neurocomputing
Shenglong Chen, Hong-Li Li, Haibo Bao, Long Zhang, Haijun Jiang, Zhiming Li
2022 conf
ASCC
Haibo Bao, Jianwei Wu
2022 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Hui Li, Yonggui Kao, Haibo Bao, YangQuan Chen
2021 J jnl
IEEE Access
Xiaoxuan Guo, Haibo Bao, Jing Xiao, Shaonan Chen
2021 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Haibo Bao, Ju H. Park, Jinde Cao
2021 J jnl
J. Frankl. Inst.
Xifen Wu, Haibo Bao, Jinde Cao
2020 J jnl
IEICE Trans. Inf. Syst.
Xiaoxuan Guo, Renxi Gong, Haibo Bao, Zhenkun Lu
2020 J jnl
Neurocomputing
Chunni Pan, Haibo Bao
2020 J jnl
Appl. Math. Comput.
Xifen Wu, Haibo Bao
2020 J jnl
J. Appl. Math. Comput.
Jie Zhou, Haibo Bao
2020 J jnl
Neurocomputing
Guoxiong Xu, Haibo Bao
2020 J jnl
IEEE Access
Jing Xiao, Xiaoxuan Guo, Yubin Feng, Haibo Bao, Ning Wu
2020 J jnl
Appl. Math. Comput.
Yao Huang, Haibo Bao
2020 J jnl
Neural Process. Lett.
Guoxiong Xu, Haibo Bao, Jinde Cao
2020 J jnl
Appl. Math. Comput.
Tawfiqullah Ayoubi, Haibo Bao
2020 J jnl
Neural Networks
Xingnan Qi, Haibo Bao, Jinde Cao
2019 J jnl
Appl. Math. Comput.
Xingnan Qi, Haibo Bao, Jinde Cao
2019 J jnl
IEEE Access
Xiaoxuan Guo, Renxi Gong, Haibo Bao, Qingyu Wang
2019 J jnl
Neural Networks
Haibo Bao, Ju H. Park, Jinde Cao
2018 J jnl
Neural Networks
Haibo Bao, Jinde Cao, Jürgen Kurths, Ahmed Alsaedi, Bashir Ahmad
2018 J jnl
Appl. Math. Comput.
Jianmei Zhang, Jianwei Wu, Haibo Bao, Jinde Cao
2016 conf
ICACI
Haibo Bao, Ju H. Park
2016 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Haibo Bao, Ju H. Park, Jinde Cao
2016 J jnl
Neural Networks
Haibo Bao, Ju H. Park, Jinde Cao
2016 J jnl
Complex.
Haibo Bao, Ju H. Park, Jinde Cao
2015 J jnl
Appl. Math. Comput.
Haibo Bao, Ju H. Park, Jinde Cao
2015 J jnl
Neural Networks
Haibo Bao, Jinde Cao
2012 J jnl
Appl. Math. Comput.
Haibo Bao, Jinde Cao
2011 J jnl
Neural Networks
Haibo Bao, Jinde Cao
2009 conf
ISNN (1)
Quanxin Cheng, Haibo Bao, Jinde Cao
2009 J jnl
Appl. Math. Comput.
Haibo Bao, Jinde Cao
redb/extractors/js_extractors/js_suspicious_apis.py
← Index redb/extractors/js_extractors/js_suspicious_apis.py python
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.js_extractor import JSExtractor
from redb.extractors.js_extractors.js_patterns import CATEGORIES, PATTERNS


# Backwards-compatible export: `{category: [(raw_pattern_string, api_name), ...]}`
# in canonical PATTERNS insertion order (code_execution, network, filesystem,
# process, registry, crypto_encoding, dom_manipulation). Kept so external
# callers (notably JSDeobfuscationExtractor pre-cleanup) keep working until
# they are migrated to PATTERNS directly.
SUSPICIOUS_APIS: "dict[str, list[tuple[str, str]]]" = {}
for _name, _compiled in PATTERNS.items():
    SUSPICIOUS_APIS.setdefault(CATEGORIES[_name], []).append((_compiled.pattern, _name))


class JSSuspiciousAPIsExtractor(JSExtractor):

    def __init__(
        self, filepath, log, exporters=None, index_prefix=None,
        known_benign=False, known_malicious=False, source=None, context=None,
    ):
        super().__init__(
            filepath, log, exporters, index_prefix,
            known_benign, known_malicious, source, context=context,
        )
        self.api_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

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

    def _get_context_snippet(self, line, max_len=200):
        """Get a truncated context snippet around a match."""
        line = line.strip()
        if len(line) > max_len:
            return line[:max_len] + "..."
        return line

    def extract(self):
        src = self.js_source
        if not src:
            return None

        # Pass 1: shared per-sample scan over the raw source. The dict contains
        # entries for both PATTERNS and FEATURE_PATTERNS; the loop below only
        # consults PATTERNS keys, so feature-only entries are ignored.
        raw_scan = self._context.scan or {}
        raw_lines = self.lines

        # Pass 2: same patterns over the deobfuscated text, when the
        # deobfuscator produced something meaningfully different. APIs hidden
        # behind one obfuscation layer (Vjw0rm-style array.join + eval,
        # Dean-Edwards packers, jjencode, ...) only surface here. The scan is
        # cached on JSContext so JSDeobfuscationExtractor (which computes the
        # new_apis_found diff) reuses the same result.
        deobf_scan = self._context.scan_deobfuscated
        if deobf_scan:
            deobf_text, _ = self._context.deobfuscated
            deobf_lines = deobf_text.splitlines()
        else:
            deobf_lines = []

        findings = []
        # Iterate PATTERNS in canonical order so output is deterministic and
        # matches the historical category/pattern ordering. For each api_name,
        # raw findings take precedence; if an API is found only in the
        # deobfuscated text, we surface it as a row tagged revealed_by_deobf=1
        # with line numbers / snippets pulled from the deobfuscated source.
        for api_name in PATTERNS:
            raw_info = raw_scan.get(api_name)
            if raw_info:
                line_numbers = raw_info["lines"]
                lines_for_snippets = raw_lines
                revealed_by_deobf = 0
            else:
                deobf_info = deobf_scan.get(api_name)
                if not deobf_info:
                    continue
                line_numbers = deobf_info["lines"]
                lines_for_snippets = deobf_lines
                revealed_by_deobf = 1

            snippets = [
                self._get_context_snippet(lines_for_snippets[ln - 1])
                for ln in line_numbers[:3]
                if 0 < ln <= len(lines_for_snippets)
            ]
            findings.append({
                "api_name": api_name,
                "api_category": CATEGORIES[api_name],
                # Historical semantics: count = number of unique lines with a
                # match, not total in-source match count.
                "call_count": len(line_numbers),
                "line_numbers": line_numbers,
                "context_snippet": " | ".join(snippets),
                "revealed_by_deobf": revealed_by_deobf,
            })

        if not findings:
            return None

        self.api_findings = findings
        return findings

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ClickHouseExporter":
            if not self.api_findings:
                return None

            current_time = datetime.now(timezone.utc)
            data = []
            for f in self.api_findings:
                data.append([
                    self.sha256,
                    f['api_name'],
                    f['api_category'],
                    f['call_count'],
                    f['line_numbers'],
                    f['context_snippet'],
                    f['revealed_by_deobf'],
                    current_time,
                ])

            column_names = [
                "sha256", "api_name", "api_category",
                "call_count", "line_numbers", "context_snippet",
                "revealed_by_deobf",
                "analysis_date",
            ]

            column_type_names = [
                "FixedString(64)", "String", "LowCardinality(String)",
                "UInt32", "Array(UInt32)", "String",
                "UInt8",
                "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

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