Xiaofeng Wang

70 papers A* 1A 1B 1C 12Journal 36Unranked 19
YearRankTypeTitle / Venue / Authors
2025 J jnl
Clust. Comput.
Linqiu Gui, Chunnian Zeng, Jie Luo, Xiaofeng Wang, Xu Yang, Shengshi Zhong
2024 J jnl
IEEE Robotics Autom. Lett.
Ran Tao, Sheng Cheng, Xiaofeng Wang, Shenlong Wang, Naira Hovakimyan
2024 J jnl
IEEE Trans. Cybern.
Ying Shen, Zheng-Guang Wu, Xiaofeng Wang
2024 conf
EMNLP (Findings)
Kai Yao, Penglei Gao, Lichun Li, Yuan Zhao, Xiaofeng Wang, Wei Wang, Jianke Zhu
2024 J jnl
CoRR
Kai Yao, Penglei Gao, Lichun Li, Yuan Zhao, Xiaofeng Wang, Wei Wang, Jianke Zhu
2024 J jnl
IEEE Trans. Wirel. Commun.
Alphan Sahin, Xiaofeng Wang
2024 J jnl
CoRR
Sheng Cheng, Ran Tao, Yuliang Gu, Shenlong Wang, Xiaofeng Wang, Naira Hovakimyan
2023 A* conf
ICCV
Rabab Abdelfattah, Qing Guo, Xiaoguang Li, Xiaofeng Wang, Song Wang
2023 J jnl
CoRR
Rabab Abdelfattah, Qing Guo, Xiaoguang Li, Xiaofeng Wang, Song Wang
2023 J jnl
CoRR
Ran Tao, Sheng Cheng, Xiaofeng Wang, Shenlong Wang, Naira Hovakimyan
2023 J jnl
IEEE Trans. Cybern.
Jie Tao, Zehui Xiao, Jiawei Chen, Ming Lin, Renquan Lu, Peng Shi, Xiaofeng Wang
2023 J jnl
IEEE Trans. Image Process.
Rabab Abdelfattah, Xiaofeng Wang, Song Wang
2022 conf
HPCC/DSS/SmartCity/DependSys
Xin Zhang, Rabab Abdelfattah, Yuqi Song, Samuel A. Dauchert, Xiaofeng Wang
2022 J jnl
CoRR
Xin Zhang, Rabab Abdelfattah, Yuqi Song, Samuel A. Dauchert, Xiaofeng Wang
2022 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Jie Tao, Zehui Xiao, Zeyu Li, Jun Wu, Renquan Lu, Peng Shi, Xiaofeng Wang
2022 J jnl
Appl. Math. Comput.
Zhenyu Wu, Jiawei Chen, Xuexi Zhang, Zehui Xiao, Jie Tao, Xiaofeng Wang
2022 J jnl
IEEE Trans. Syst. Man Cybern. Syst.
Jie Tao, Zehui Xiao, Hong-Xia Rao, Jun Wu, Renquan Lu, Peng Shi, Xiaofeng Wang
2022 A conf
BMVC
Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, Xiaofeng Wang, Song Wang
2022 J jnl
CoRR
Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, Xiaofeng Wang, Song Wang
2022 J jnl
CoRR
Rabab Abdelfattah, Xiaofeng Wang, Song Wang
2022 J jnl
CoRR
Rabab Abdelfattah, Xin Zhang, Zhenyao Wu, Xinyi Wu, Xiaofeng Wang, Song Wang
2022 conf
ECCV Workshops (2)
Rabab Abdelfattah, Xin Zhang, Zhenyao Wu, Xinyi Wu, Xiaofeng Wang, Song Wang
2021 C conf
ACC
Lixing Yang, Samuel A. Dauchert, Xiaofeng Wang
2021 J jnl
IEEE Trans. Syst. Man Cybern. Syst.
Jie Tao, Chaoyou Wei, Jun Wu, Xiaofeng Wang, Peng Shi
2020 conf
CDC
Pan Zhao, Yanbing Mao, Chuyuan Tao, Naira Hovakimyan, Xiaofeng Wang
2020 J jnl
CoRR
Pan Zhao, Yanbing Mao, Chuyuan Tao, Naira Hovakimyan, Xiaofeng Wang
2020 J jnl
IEEE Trans. Autom. Control.
Jie Tao, Lixing Yang, Zheng-Guang Wu, Xiaofeng Wang, Hongye Su
2020 conf
ACCV (6)
Rabab Abdelfattah, Xiaofeng Wang, Song Wang
2020 J jnl
CoRR
Rabab Abdelfattah, Xiaofeng Wang, Song Wang
2019 J jnl
IEEE Trans. Ind. Electron.
Wuzhao Yan, Bin Zhang, Guangquan Zhao, Shijie Tang, Guangxing Niu, Xiaofeng Wang
2018 conf
CDC
Ying Shen, Xiaofeng Wang, Zheng-Guang Wu
2018 J jnl
IEEE Trans. Autom. Control.
Zheqing Zhou, Xiaofeng Wang
2018 C conf
ACC
Lixing Yang, Xiaofeng Wang
2018 J jnl
ACM Trans. Cyber Phys. Syst.
Xiaofeng Wang, Naira Hovakimyan, Lui Sha
2017 J jnl
IEEE Trans. Autom. Control.
Lichun Li, Xiaofeng Wang, Michael D. Lemmon
2017 C conf
ACC
Xiaofeng Wang, Lixing Yang
2016 conf
CDC
Bohui Wang, Jingcheng Wang, Huifeng Zhu, Bin Zhang, Xiaocheng Li, Xiaofeng Wang, Leijie Dai, Ruiqing Wang
2016 J jnl
Autom.
Xiaofeng Wang, Bin Zhang
2016 J jnl
IEEE Trans. Ind. Electron.
Wuzhao Yan, Bin Zhang, Xiaofeng Wang, Wanchun Dou, Jingcheng Wang
2015 conf
CDC
Xiaofeng Wang, Zheqing Zhou
2015 J jnl
IEEE Trans. Autom. Control.
Xiaofeng Wang, Evgeny Kharisov, Naira Hovakimyan
2014 C conf
ACC
Xiaofeng Wang, Yu Sun, Kun Deng
2014 conf
CDC
Xiaofeng Wang, Bin Zhang
2014 J jnl
Discret. Event Dyn. Syst.
Yu Sun, Xiaofeng Wang
2013 J jnl
IEEE Trans. Autom. Control.
Xiaofeng Wang, Naira Hovakimyan
2013 C conf
ACC
Xiaofeng Wang, Evgeny Kharisov, Naira Hovakimyan
2013 conf
ICCPS
Xiaofeng Wang, Naira Hovakimyan, Lui Sha
2012 C conf
ACC
Xiaofeng Wang, Naira Hovakimyan
2012 J jnl
Syst. Control. Lett.
Xiaofeng Wang, Yu Sun, Naira Hovakimyan
2012 J jnl
Syst. Control. Lett.
Xiaofeng Wang, Naira Hovakimyan
2012 conf
ADHS
Lichun Li, Xiaofeng Wang, Michael Lemmon
2012 conf
HSCC
Lichun Li, Xiaofeng Wang, Michael D. Lemmon
2011 conf
CDC/ECC
Xiaofeng Wang, Michael D. Lemmon
2011 J jnl
IEEE Trans. Autom. Control.
Xiaofeng Wang, Michael D. Lemmon
2011 J jnl
Autom.
Xiaofeng Wang, Michael D. Lemmon
2011 C conf
ACC
Xiaofeng Wang, Naira Hovakimyan
2010 C conf
ACC
Xiaofeng Wang, Michael D. Lemmon
2010 C conf
ACC
Lichun Li, Michael Lemmon, Xiaofeng Wang
2010 conf
CDC
Xiaofeng Wang, Naira Hovakimyan
2010 conf
CDC
Xiaofeng Wang, Yu Sun, Naira Hovakimyan
2010 J jnl
IEEE Trans. Autom. Control.
Xiaofeng Wang, Michael D. Lemmon
2010 C conf
ACC
Xiaofeng Wang, Naira Hovakimyan
2009 conf
HSCC
Xiaofeng Wang, Michael D. Lemmon
2009 conf
ECC
Xiaofeng Wang, Michael D. Lemmon
2009 J jnl
IEEE Trans. Autom. Control.
Xiaofeng Wang, Michael D. Lemmon
2009 C conf
ACC
Xiaofeng Wang, Michael D. Lemmon
2008 conf
HSCC
Xiaofeng Wang, Michael D. Lemmon
2008 conf
CDC
Xiaofeng Wang, Michael D. Lemmon
2008 C conf
ACC
Xiaofeng Wang, Michael D. Lemmon
2008 B conf
ECRTS
Thidapat Chantem, Xiaofeng Wang, Michael D. Lemmon, Xiaobo Sharon Hu
redb/extractors/js_extractors/js_strings.py
← Index redb/extractors/js_extractors/js_strings.py python
import base64
import bisect
import inspect
import re
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 STRING_PATTERNS, line_offsets

# Local aliases for the compiled patterns this extractor uses. Defined and
# compiled exactly once in js_patterns.STRING_PATTERNS.
_HEX_STRING_RE = STRING_PATTERNS["hex_escape_seq"]
_UNICODE_STRING_RE = STRING_PATTERNS["unicode_escape_seq"]
_CHARCODE_RE = STRING_PATTERNS["charcode_call"]
_BASE64_STRING_RE = STRING_PATTERNS["base64_quoted"]
_CONCAT_STRING_RE = STRING_PATTERNS["concat_chain"]

# Tokeniser used inside _reconstruct_concat to pull each quoted part out of a
# matched concat chain. Compiled once at module load (was recompiled on every
# concat match before).
_CONCAT_TOKEN_RE = re.compile(r'["\']([^"\']*)["\']')


class JSStringsExtractor(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.string_findings = None
        self.log.debug(inspect.currentframe().f_code.co_name)

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

    def _decode_hex_string(self, hex_str):
        """Decode \\x41\\x42 style hex strings."""
        try:
            # Remove \\x prefix and decode
            clean = hex_str.replace('\\x', '')
            return bytes.fromhex(clean).decode('utf-8', errors='replace')
        except Exception:
            return None

    def _decode_unicode_string(self, uni_str):
        """Decode \\u0041\\u0042 style unicode strings."""
        try:
            return uni_str.encode('utf-8').decode('unicode_escape')
        except Exception:
            return None

    def _decode_charcode(self, charcode_str):
        """Decode String.fromCharCode(72, 101, 108, ...) sequences."""
        try:
            codes = [int(c.strip()) for c in charcode_str.split(',') if c.strip().isdigit()]
            return ''.join(chr(c) for c in codes if 0 <= c <= 0x10FFFF)
        except Exception:
            return None

    def _decode_base64(self, b64_str):
        """Attempt to decode base64 string."""
        try:
            decoded = base64.b64decode(b64_str)
            # Check if result is printable text
            text = decoded.decode('utf-8', errors='strict')
            # Only return if it looks like text (>80% printable)
            printable = sum(1 for c in text if c.isprintable() or c in '\n\r\t')
            if printable / len(text) > 0.8:
                return text
        except Exception:
            pass
        return None

    def _reconstruct_concat(self, concat_match):
        """Reconstruct concatenated string parts."""
        try:
            parts = _CONCAT_TOKEN_RE.findall(concat_match)
            return ''.join(parts)
        except Exception:
            return None

    def _find_line_number(self, match_start):
        """1-indexed line number for `match_start`, looked up in O(log L) via
        bisect over `self._line_offsets` (built once per extract() call).

        Replaces the historical `self.js_source[:match_start].count('\\n') + 1`
        which was O(N) per call and quadratic across all matches in a sample.
        """
        return bisect.bisect_right(self._line_offsets, match_start)

    def _scan_text(self, text):
        """Run every encoded-string pattern over `text` and return a list of
        finding dicts. Stateless apart from the per-call `_line_offsets` cache,
        which `_find_line_number` reads — callers must reset it before invoking
        this so line numbers reference the text being scanned, not the previous
        one.
        """
        findings = []

        # Hex-encoded strings
        for m in _HEX_STRING_RE.finditer(text):
            raw = m.group()
            decoded = self._decode_hex_string(raw)
            if decoded and len(decoded) >= 4:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'hex',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # Unicode-encoded strings
        for m in _UNICODE_STRING_RE.finditer(text):
            raw = m.group()
            decoded = self._decode_unicode_string(raw)
            if decoded and len(decoded) >= 3:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'unicode',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # String.fromCharCode sequences
        for m in _CHARCODE_RE.finditer(text):
            raw = m.group()
            decoded = self._decode_charcode(m.group(1))
            if decoded and len(decoded) >= 4:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'charcode',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # Base64-encoded strings
        for m in _BASE64_STRING_RE.finditer(text):
            raw = m.group(0)
            b64_val = m.group(1)
            decoded = self._decode_base64(b64_val)
            if decoded and len(decoded) >= 10:
                findings.append({
                    'string': decoded[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'base64',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(decoded),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(decoded),
                })

        # Concatenated strings (reassembled)
        for m in _CONCAT_STRING_RE.finditer(text):
            raw = m.group()
            reconstructed = self._reconstruct_concat(raw)
            if reconstructed and len(reconstructed) >= 20:
                findings.append({
                    'string': reconstructed[:4000],
                    'string_raw': raw[:4000],
                    'string_encoding': 'concat',
                    'string_offset': self._find_line_number(m.start()),
                    'string_length': len(reconstructed),
                    'string_raw_length': len(raw),
                    'string_entropy': self._calculate_text_entropy(reconstructed),
                })

        return findings

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

        # Pass 1: raw source. _line_offsets is keyed off whichever text is
        # currently being scanned so _find_line_number resolves to that text.
        self._line_offsets = line_offsets(src)
        findings = self._scan_text(src)

        # Pass 2: deobfuscated text, when the deobfuscator produced something
        # meaningfully different. Same patterns, but a different surface — for
        # samples where the encoded payload is hidden behind an outer wrapper
        # (e.g. array.join() + eval in Vjw0rm/WSH-RAT) only this pass yields
        # any rows at all.
        deobf_text, _ = self._context.deobfuscated
        if deobf_text and deobf_text != src:
            self._line_offsets = line_offsets(deobf_text)
            findings.extend(self._scan_text(deobf_text))

        if not findings:
            return None

        # Deduplicate by decoded string value (raw pass wins on collision: it
        # comes first in `findings`). A string that surfaces only in the
        # deobfuscated text still gets persisted, which is the whole point of
        # the second pass.
        seen_values = set()
        deduped = []
        for f in findings:
            val_key = f['string'][:100]
            if val_key not in seen_values:
                seen_values.add(val_key)
                deduped.append(f)

        self.string_findings = deduped[:500]  # Limit per file
        # Publish to the shared context so post-loop consumers (notably the IOC
        # plumbing in workers.py) can scrape the decoded strings without
        # holding a reference to this extractor instance.
        self._context.decoded_strings = self.string_findings
        return self.string_findings

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

            data = []
            for f in self.string_findings:
                data.append([
                    self.sha256,
                    f['string'],
                    f['string_raw'],
                    f['string_encoding'],
                    f['string_offset'],
                    f['string_length'],
                    f['string_raw_length'],
                    f['string_entropy'],
                ])

            column_names = [
                "sha256",
                "string",
                "string_raw",
                "string_encoding",
                "string_offset",
                "string_length",
                "string_raw_length",
                "string_entropy",
            ]

            column_type_names = [
                "FixedString(64)",
                "String",
                "String",
                "LowCardinality(String)",
                "UInt64",
                "UInt32",
                "UInt32",
                "Float32",
            ]

            return (data, column_names, column_type_names)

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