Xiaofen Li

18 papers Journal 8Unranked 10
YearRankTypeTitle / Venue / Authors
2025 J jnl
IEEE Trans. Instrum. Meas.
Meimei Lu, Helei Dong, Yinrong Zheng, Juan Zhang, Xiaofen Li, Qiulin Tan
2024 J jnl
BMC Medical Imaging
Xiaofen Li, Jingkun Zhang, Yinping Leng, Jiaqi Liu, Linlin Li, Tianyi Wan, Wentao Dong, Bing Fan, Lianggeng Gong
2024 J jnl
IEEE Trans. Instrum. Meas.
Xiaofen Li, Lei Zhang, Hao Xie, Ze Shang, Xiangxiang Zhang, Xue Qi, Meimei Lu, Qiulin Tan
2022 conf
ICEMT
Qi Huang, Xiaofen Li, Hailing Liu
2022 conf
IC4E
Xiaofen Li, Manni Wei, Ying Zhuo
2022 conf
ICEME
Qiuwen Qin, Xiaofen Li, Jing Li
2022 conf
IC4E
Xiaofen Li, Wanbin Nong
2021 conf
ICETT
Qi Huang, Xiaofen Li, Qunjuan Xiang
2021 conf
ICEME
Qi Huang, Xiaofen Li, Hailing Liu
2021 conf
ICEBT
Qi Huang, Xiaofen Li, Hailing Liu
2021 conf
EBEE
Wanbin Nong, Xiaofen Li
2020 conf
ICETT
Xiaofen Li, Qi Huang
2019 J jnl
Trans. Edutainment
Xiaofen Li
2018 J jnl
Trans. Edutainment
Xiaofen Li, Defa Zhang
2018 conf
ICME Workshops
Xiaofen Li, Yongqing Huo
2017 J jnl
计算机科学
Wei Xu, Xiaofen Li, Duanyang Liu
2016 J jnl
Sensors
Xiaoyan Ma, Hongqiao Yang, Huabin Xiong, Xiaofen Li, Jinting Gao, Yuntao Gao
2015 J jnl
计算机科学
Duanyang Liu, Jian Feng, Xiaofen Li
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"