Xiao Yang

20 papers A* 1B 1C 3Misc 1Journal 8Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
IEEE Trans. Comput. Soc. Syst.
Xiao Yang, Gaolei Li, Xinzheng Feng, Xiaoyu Yi, Jianhua Li
2026 J jnl
IEEE Wirel. Commun. Lett.
Xiao Yang, Yuanhang He, Gaolei Li, Jianhua Li, Yang Xu, Yuchen Liu, Mingzhe Chen
2026 J jnl
IEEE Trans. Mob. Comput.
Xiao Yang, Yuni Lai, Gaolei Li, Jun Wu, Kai Zhou, Mingzhe Chen
2025 J jnl
Inf. Fusion
Xiao Yang, Gaolei Li, Kai Zhou, Yuni Lai, Jianhua Li
2025 J jnl
IEEE Trans. Consumer Electron.
Xiao Yang, Gaolei Li, Jun Wu, Kai Zhou, Jianhua Li, Wu Yang
2025 A* conf
IJCAI
Xiao Yang, Yuni Lai, Kai Zhou, Gaolei Li, Jianhua Li, Hang Zhang
2025 B conf
TrustCom
Yibin Zhou, Yunxiao Shi, Xiao Yang, Da Xiao, Gaolei Li, Jianhua Li
2025 Misc conf
ICNC
Wenhui Du, Yuanhang He, Gaolei Li, Xiao Yang, Jianhua Li, Ge Ren, Kai Zhou
2025 J jnl
CoRR
Zhihan Cao, Xiao Yang, Gaolei Li, Jun Wu, Jianhua Li, Yuchen Liu
2025 conf
ICC
Xiao Yang, Yuanhang He, Gaolei Li, Jianhua Li
2024 C conf
ISPA
Xiao Yang, Gaolei Li, Mianxiong Dong, Kaoru Ota, Chaofeng Zhang, Xiting Peng, Jianhua Li
2024 J jnl
CoRR
Xiao Yang, Gaolei Li, Jianhua Li
2024 C conf
ISPA
Xiao Yang, Gaolei Li, Mianxiong Dong, Kaoru Ota, Jun Wu, Jianhua Li
2023 conf
ICA3PP (5)
Xiao Yang, Gaolei Li, Xiaoyi Tao, Chaofeng Zhang, Jianhua Li
2023 C conf
ICCC
Xiao Yang, Mianxiong Dong, Kaoru Ota, Gaolei Li, Chaofeng Zhang
2023 conf
AISafety/SafeRL@IJCAI
Xiao Yang, Gaolei Li, Chaofeng Zhang, Meng Han, Wu Yang
2023 conf
ICDF2C (1)
Xiao Yang, Gaolei Li, Meng Han
2023 conf
WCSP
Rui Xu, Gaolei Li, Zhaohui Yang, Xiao Yang, Jianhua Li
2020 conf
MSN
Xiao Yang, Chaofeng Zhang, Caijuan Chen, Haozhe Liang
2020 J jnl
IEEE Trans. Comput. Soc. Syst.
Xiao Yang, Mianxiong Dong, Xiuzhen Chen, Kaoru Ota
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"