Xiaojia Wang

38 papers Journal 31Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
Inf. Fusion
Chunfeng Yang, Bohui Yang, Jie Li, Xiaojia Wang, Jianqing Li, Eugene Yu-Dong Zhang, Yang Chen, Angelo Cangelosi, Wentao Xiang
2026 J jnl
Appl. Math. Comput.
Xiaozhou Feng, Yang Lei, Fei Xie, Changtong Li, Yuzhen Wang, Xiaojia Wang, Tingting Li
2026 J jnl
Biomed. Signal Process. Control.
Chunfeng Yang, Jiacheng Wei, Jianqing Li, Bin Liu, Régine Le Bouquin-Jeannès, Liangcheng Qu, Kuiying Yin, Xiaojia Wang, Wentao Xiang
2025 J jnl
Int. J. Comput. Intell. Syst.
Zhizhen Liang, Xiaojia Wang, Sheng Xu, Wei Chen
2025 J jnl
IEEE Trans. Comput. Soc. Syst.
Xiaojia Wang, Zirui Xue, Ying Chen
2025 J jnl
Brain Informatics
Xiaojia Wang, Dayang Wu, Chunfeng Yang
2025 J jnl
IEEE Trans. Sustain. Comput.
Xiaojia Wang, Ya Chen, Haipeng Yao
2025 J jnl
IEEE Trans. Instrum. Meas.
Feng Zhang, Chunfeng Yang, Linlin You, Xiaojia Wang, Yonggui Yuan, Régine Le Bouquin Jeannes, Huazhong Shu, Wentao Xiang
2024 J jnl
IEEE Trans. Comput. Soc. Syst.
Xiaojia Wang, Ziqing Luo, Ying Chen
2024 J jnl
IEEE Trans. Wirel. Commun.
Xiaojia Wang, Suzhi Bi, Xian Li, Xiaohui Lin, Zhi Quan, Ying-Jun Angela Zhang
2024 J jnl
Int. J. Intell. Comput. Cybern.
Keqing Li, Xiaojia Wang, Changyong Liang, Wenxing Lu
2024 conf
PRCV (11)
Xiaojia Wang, Mingliang Zhang, Bin Li
2024 J jnl
Brain Informatics
Xiaojia Wang, Yanchao Liu, Chunfeng Yang
2024 J jnl
IEEE Access
Xiaojia Wang, Weijian Xia, Lushi Yao, Xibin Zhao
2023 J jnl
Symmetry
Xiaojia Wang, Cunjia Wang, Keyu Zhu, Xibin Zhao
2023 J jnl
IEEE Trans. Cogn. Dev. Syst.
Bin Fang, Wenlong Ding, Fuchun Sun, Jianhua Shan, Xiaojia Wang, Chengyin Wang, Xinyu Zhang
2023 J jnl
CoRR
Xiaojia Wang, Suzhi Bi, Xian Li, Xiaohui Lin, Zhi Quan, Ying-Jun Angela Zhang
2023 conf
ICC
Xiaojia Wang, Suzhi Bi, Xian Li, Zheyuan Yang, Xiao-Hui Lin, Zhi Quan, Ying-Jun Angela Zhang
2023 J jnl
Int. J. Inf. Syst. Supply Chain Manag.
Xiaojia Wang, Richard Y. K. Fung
2022 J jnl
Int. J. Comput. Intell. Syst.
Xiaojia Wang, Yurong Wang, Shanshan Zhang, Lushi Yao, Sheng Xu
2022 J jnl
Int. J. Comput. Intell. Syst.
Xiaojia Wang, Rong Wang, Shanshan Zhang, Junhang Liu, Li Jiang
2021 J jnl
Expert Syst. J. Knowl. Eng.
Xiaojia Wang, Peiling Cheng, Keyu Zhu, Sheng Xu, Shanshan Zhang, Weiqun Xu, Yuxiang Guan
2021 J jnl
Int. J. Comput. Intell. Syst.
Xiaojia Wang, Zhizhen Liang, Keyu Zhu
2021 J jnl
Int. J. Comput. Intell. Syst.
Xiaojia Wang, Kuo Du, Keyu Zhu, Shen Xu, Shanshan Zhang
2020 J jnl
Expert Syst. J. Knowl. Eng.
Xiaojia Wang, Mi Chen, Wei Xia, Keyu Zhu, Shanshan Zhang, Weiqun Xu, Yuxiang Guan
2020 J jnl
Int. J. Comput. Intell. Syst.
Xiaojia Wang, Wenqing Gong, Keyu Zhu, Lushi Yao, Shanshan Zhang, Weiqun Xu, Yuxiang Guan
2020 J jnl
IEEE Access
Xiaojia Wang, Changyan Shao, Sheng Xu, Shanshan Zhang, Weiqun Xu, Yuxiang Guan
2019 J jnl
BMC Medical Informatics Decis. Mak.
Xiaojia Wang, Linglan He, Keyu Zhu, Shanshan Zhang, Ling Xin, Weiqun Xu, Yuxiang Guan
2019 conf
ICITEE
Rui Zhang, Xiaojia Wang
2018 J jnl
J. Oper. Res. Soc.
Xiaojia Wang, Wei Chen, Jennifer Shang, Shanlin Yang
2018 conf
MWSCAS
Xiaojia Wang, Yingjie Lao
2017 conf
3DUI
Nathanael Harrell, Grayson Bonds, Xiaojia Wang, Sean Valent, Elham Ebrahimi, Sabarish V. Babu
2017 J jnl
IEICE Trans. Commun.
Xiaojia Wang, Yazhou Chen, Haojiang Wan, Qingxi Yang
2016 J jnl
IEICE Trans. Commun.
Xiaojia Wang, Yazhou Chen, Haojiang Wan, Lipeng Wang, Qingxi Yang
2012 conf
BMEI
Xin Zhang, Anchun Cheng, Mingshu Wang, Dekang Zhu, Xiaojia Wang, RenYong Jia, Xiaoyue Chen
2012 conf
BMEI
Pan Xu, Dekang Zhu, Xiaojia Wang, Xiaoyue Chen, Anchun Cheng, Mingshu Wang
2010 J jnl
Int. J. Humanoid Robotics
Qirong Mao, Xiaojia Wang, Yongzhao Zhan
2005 J jnl
Int. J. Inf. Acquis.
Jun Gao, Xiaojia Wang, Johannes Eckstein, Peter Ott
redb/extractors/js_extractors/scripts/js-xray-runner.js
← Index redb/extractors/js_extractors/scripts/js-xray-runner.js javascript
#!/usr/bin/env node
// Bridge between the Python JS pipeline and @nodesecure/js-x-ray.
//
// Usage: node js-xray-runner.js <path-to-js-file>
//   stdout  one JSON object: {"obfuscator": <name|null>, "warnings": [...]}
//   stderr  human-readable error on failure
//   exit 0  analysis ran (the file may still be benign — see "obfuscator")
//   exit 1  the file could not be read or analysed
//
// Each warning is emitted as {kind, value} so the Python side can tag
// supporting signals (encoded-literal, short-identifiers, suspicious-literal,
// unsafe-stmt) without having to mirror js-x-ray's whole schema.
//
// js-x-ray ≥7 ships as an ES module, which CommonJS `require()` cannot load
// from a `.js` script — the dynamic `import()` below is what makes the
// bridge work without renaming the file to `.mjs` or adding `"type":
// "module"` to package.json (which would break tools that still
// `require()` from this directory).

const fs = require("fs");
const path = require("path");

function fail(msg) {
  process.stderr.write(msg + "\n");
  process.exit(1);
}

async function main() {
  const target = process.argv[2];
  if (!target) fail("usage: js-xray-runner.js <file>");

  let source;
  try {
    source = fs.readFileSync(target, "utf8");
  } catch (e) {
    fail(`read failed: ${e.message}`);
  }

  // The legacy `runASTAnalysis` function is deprecated (removed in v8); the
  // current API is the `AstAnalyser` class. Both produce a result with the
  // same `warnings` shape, so the rest of the bridge is unchanged.
  let AstAnalyser;
  try {
    ({ AstAnalyser } = await import("@nodesecure/js-x-ray"));
  } catch (e) {
    fail(`@nodesecure/js-x-ray not installed (run \`npm install\` in ${path.dirname(__filename)}): ${e.message}`);
  }

  // js-x-ray defaults to module-mode parsing, which rejects scripts that
  // (legally) use reserved words as identifiers, top-level `return`, etc.
  // A lot of real-world JS malware is script-style (WScript/HTA bodies,
  // pasted snippets) — retrying in script mode catches those without
  // pulling in a more lenient parser. Both attempts share the same
  // analyser; only the parse mode flips. If both fail, the original error
  // (module-mode) is reported because that's the more informative one for
  // genuinely broken sources.
  let result;
  const analyser = new AstAnalyser();
  let firstErr;
  try {
    result = await analyser.analyse(source, { module: true });
  } catch (e) {
    firstErr = e;
    try {
      result = await analyser.analyse(source, { module: false });
    } catch (e2) {
      fail(`js-x-ray analysis failed: ${firstErr.message}`);
    }
  }

  const warnings = (result.warnings || []).map((w) => ({
    kind: w.kind,
    value: w.value !== undefined ? w.value : null,
  }));

  // js-x-ray flags the obfuscator family in a warning whose kind is
  // "obfuscated-code" and whose value names the family (jsfuck, obfuscator.io,
  // freejsobfuscator, morse, jjencode, ...). Absent => not detected.
  const obfWarning = warnings.find((w) => w.kind === "obfuscated-code");
  const obfuscator = obfWarning ? obfWarning.value : null;

  // js-x-ray runs its own AST internally with a modern parser, so its
  // identifier-length average is the only path the Python pipeline has to
  // that signal on ES2015+ sources — pyjsparser is ES5.1-only and silently
  // drops to 0 the moment it hits destructuring, classes, optional chaining,
  // etc. Surfacing this lets the heuristic's `avg_identifier_length<2`
  // strong signal fire on real obfuscator.io output. `null` when the value
  // is missing or non-numeric (defensive — older js-x-ray builds may differ).
  const idsLengthAvg =
    typeof result.idsLengthAvg === "number" && !Number.isNaN(result.idsLengthAvg)
      ? result.idsLengthAvg
      : null;

  process.stdout.write(JSON.stringify({ obfuscator, warnings, idsLengthAvg }));
}

main().catch((e) => fail(e.message || String(e)));