Xiaodong Jia

18 papers C 1Journal 13Unranked 4
YearRankTypeTitle / Venue / Authors
2025 J jnl
IEEE Trans. Instrum. Meas.
Marcella Miller, Xu Han, Gregory W. Vogl, Anita Penkova, Xiaodong Jia
2025 J jnl
IEEE Trans. Biomed. Eng.
Varun Vinayak Kalaiarasan, Marcella Miller, Xu Han, Brandon Foreman, Xiaodong Jia
2025 conf
CASE
James R. Moyne, Xiaodong Jia, Jing Shi
2025 conf
ICDM (Workshops)
Xiaodong Jia, Tarek Yahia, Kody Haubeil, David Siegel, Donald Davis
2025 J jnl
CoRR
Xiaodong Jia, Qingguo Li, Wei Luan
2024 conf
ICCCV
Jingjing Zhang, Zhengnan Guo, Jiale Dong, Hao Zhang, Xiaodong Jia, Jing He, Zhi Xiong
2024 J jnl
CoRR
Hualin Miao, Huijun Hou, Xiaodong Jia, Qingguo Li
2024 J jnl
Remote. Sens.
Xiaodong Jia, Zhuoyang Li, Jiangtao Han, Hesheng Hou, Zhonghua Xin, Lijia Liu, Wenyu Liu
2023 J jnl
J. Comput. Syst. Sci.
Sanjay Jain, Xiaodong Jia, Ammar Fathin Sabili, Frank Stephan
2023 J jnl
IEEE Trans. Multim.
Bowen Ma, Tong Jia, Min Su, Xiaodong Jia, Dongyue Chen, Yichun Zhang
2022 J jnl
Briefings Bioinform.
Xianglin Zhang, Xiaodong Jia, Bixi Zhong, Lei Wei, Jiaqi Li, Wei Zhang, Huan Fang, Yanda Li, Yinying Lu, Xiaowo Wang
2022 J jnl
J. Appl. Math.
Xiaodong Jia
2021 J jnl
CoRR
Sanjay Jain, Xiaodong Jia, Ammar Fathin Sabili, Frank Stephan
2018 J jnl
IEEE Trans. Reliab.
Hongliang Liang, Xiaoxiao Pei, Xiaodong Jia, Wuwei Shen, Jian Zhang
2017 J jnl
Comput. Secur.
Hongliang Liang, Mingyu Li, Jian Xu, Wenying Hu, Xiaoxiao Pei, Xiaodong Jia, Yan Song
2016 conf
CSCloud
Hongliang Liang, Daijie Zhang, Xiaoxiao Pei, Xiaodong Jia, Guangyuan Li, Jiuyun Xu
2016 C conf
SNPD
Hongliang Liang, Daijie Zhang, Xiaodong Jia, Xiaoxiao Pei, Guangyuan Li
2011 J jnl
Bioinform.
Xiujie Chen, Jiankai Xu, Bangqing Huang, Jin Li, Xin Wu, Ling Ma, Xiaodong Jia, Xiusen Bian, Fujian Tan, Lei Liu, Sheng Chen, Xia 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"