Weina Zhang

40 papers B 3C 3Journal 24Unranked 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
Reliab. Eng. Syst. Saf.
Weina Zhang, Dalin Zhang, Jintao Liu, Lianying He
2026 J jnl
Appl. Soft Comput.
Baonan Wang, Xiaowan Yu, Zhongqin Bi, Weina Zhang, Zhe Liu, Dan Zhang
2026 J jnl
Int. J. Web Inf. Syst.
Weina Zhang, Fangyu Liu, Zhe Xu, Zhongqin Bi
2026 J jnl
Earth Sci. Informatics
Zhenwei Niu, Chenyang Zhu, Weina Zhang, Tianyu Gao, Yunxin Xie, Fang Wang
2026 J jnl
Comput. Geosci.
Zhongqin Bi, Bin Zhang, Weina Zhang, Chen Wang
2025 C conf
SEKE
Zhongqin Bi, Meiyun Xiang, Weina Zhang
2025 B conf
SMC
Zhongqin Bi, Yikun Guo, Weina Zhang, Dan Dai
2025 J jnl
Discov. Comput.
Zhongqin Bi, Xueni Hu, Weina Zhang, Xiaoyu Wang
2025 J jnl
Int. J. Web Inf. Syst.
Zhongqin Bi, Xueni Hu, Weina Zhang, Xiaoyu Wang
2025 conf
BIBM
Lin Tong, Zhaochen Su, Weina Zhang, Xinying Li, Bing Li, Ziling Zeng, Sihong Liu, Junze Ye, Danping Zheng, Lei Zhang, Hongtao Li, Huamin Zhang
2025 C conf
APSEC
Weina Zhang, Dalin Zhang, Zhenguo Ding
2025 B conf
IJCNN
Zhongqin Bi, Haojin Lu, Weina Zhang, Dan Dai
2024 conf
ICPCSEE (1)
Xiaohan Guo, Haizhou Du, Weina Zhang
2024 J jnl
Int. J. Web Inf. Syst.
Zhongqin Bi, Susu Sun, Weina Zhang, Meijing Shan
2024 J jnl
Expert Syst. Appl.
Ying-Yi Hong, Christian Lian Paulo P. Rioflorido, Weina Zhang
2024 J jnl
Appl. Soft Comput.
Zhongqin Bi, Xiaoting Yang, Baonan Wang, Weina Zhang, Zhen Dong, Dan Zhang
2024 conf
ISAIMS
Jingyu Hou, Jianhui Wan, Weina Zhang, Liyun Zhong
2024 C conf
SEKE
Zhongqin Bi, Xiaoyu Wang, Weina Zhang, Xueni Hu
2024 J jnl
Multim. Tools Appl.
Zhongqin Bi, Huanfeng Li, Weina Zhang, Zhen Dong
2023 conf
CollaborateCom (3)
Zhongqin Bi, Yutang Duan, Weina Zhang, Meijing Shan
2023 J jnl
IEEE Access
Ying-Yi Hong, Li-Fan Chen, Weina Zhang
2022 J jnl
BMC Medical Imaging
Jianmei Liao, Shuping Yang, Keyue Chen, Huijun Chen, Fan Jiang, Weina Zhang, Xuebin Wu
2022 conf
ICBET
Weina Zhang, Yilun Zhang, Xianglin Huang
2021 J jnl
Knowl. Based Syst.
Weina Zhang, Xingming Zhang, Dongpei Chen
2021 conf
ICBBT
Weina Zhang, Yilun Zhang, Xianglin Huang
2020 J jnl
Mob. Networks Appl.
Weina Zhang, Ruijuan Zheng, Mingchuan Zhang, Junlong Zhu, Qingtao Wu
2020 J jnl
Remote. Sens.
Rui Dong, Yuxin Miao, Xinbing Wang, Zhichao Chen, Fei Yuan, Weina Zhang, Haigang Li
2020 J jnl
J. Comput. Biol.
Weina Zhang, Yilun Zhang
2020 conf
SpatialDI
Te Qi, Weina Zhang, Tao Yuan
2020 J jnl
Neurocomputing
Dongpei Chen, Xingming Zhang, Haoxiang Wang, Weina Zhang
2019 J jnl
Neurocomputing
Weina Zhang, Xingming Zhang, Haoxiang Wang, Dongpei Chen
2019 J jnl
IEEE Access
Weina Zhang, Xingming Zhang, Haoxiang Wang
2019 B conf
ICTAI
Weina Zhang, Xingming Zhang, Haoxiang Wang
2018 conf
DSC
Yong Chen, Bin Zhou, Weina Zhang, Wenjie Gong, Guangfu Sun
2016 J jnl
Manag. Sci.
Sumit Agarwal, Vincent Y. S. Chen, Weina Zhang
2013 J jnl
Sensors
Qing Lu, Weina Zhang, Zhihui Wang, Guangxia Yu, Yuan Yuan, Yikai Zhou
2012 J jnl
Sensors
Weina Zhang, Wei Quan, Lei Guo
2012 J jnl
Eur. J. Oper. Res.
Joel Weiqiang Goh, Kian Guan Lim, Melvyn Sim, Weina Zhang
2012 conf
CSO
Weina Zhang, Shuang Feng, Hua Li
2012 conf
IIP
Shuang Feng, Weina Zhang
redb/extractors/js_extractors/js_context.py
← Index redb/extractors/js_extractors/js_context.py python
"""Per-sample shared state for the JavaScript extractor pipeline.

A `JSContext` is built exactly once per JS sample (in `workers.py`) and threaded
into every extractor that runs against that sample. It owns the disk read, the
decoded source text, the line-split cache, the Shannon text-entropy figure, the
shared `scan_source()` results, and the pyjsparser AST. Each of those is
computed lazily through `cached_property` so an extractor that doesn't need a
particular artefact does not pay for it.

Without this object, every JS extractor instance redoes the same disk read,
decode, scan, and (for any consumer) AST parse. With it, every extractor
shares one set of results.

`JSExtractor.__init__` accepts the context via a `context=` kwarg; if absent
(e.g. unit tests instantiating an extractor directly with `source=...`) it
builds a fresh context from the constructor arguments. Either path produces a
fully-populated context, so extractor code can always rely on
`self._context.scan` / `self._context.ast` / etc.
"""

from __future__ import annotations

import math
from collections import Counter
from dataclasses import dataclass
from functools import cached_property
from typing import Any, Dict, List, Optional

import chardet

from redb.extractors.js_extractors.js_patterns import scan_source


def decode_source(raw_bytes: bytes) -> str:
    """Decode raw JS bytes to text, honouring BOMs and falling back to chardet.

    Mirrors the historical `JSExtractor._decode_source` logic so existing tests
    continue to round-trip identically.
    """
    if not raw_bytes:
        return ""

    if raw_bytes[:3] == b"\xef\xbb\xbf":
        return raw_bytes[3:].decode("utf-8", errors="replace")
    if raw_bytes[:2] in (b"\xff\xfe", b"\xfe\xff"):
        return raw_bytes.decode("utf-16", errors="replace")

    try:
        return raw_bytes.decode("utf-8")
    except UnicodeDecodeError:
        pass

    try:
        detected = chardet.detect(raw_bytes)
        if detected and detected.get("encoding"):
            return raw_bytes.decode(detected["encoding"], errors="replace")
    except Exception:
        pass

    return raw_bytes.decode("latin-1", errors="replace")


def _text_entropy(text: str) -> float:
    """Shannon entropy of the character distribution of `text`, rounded to 4dp."""
    if not text:
        return 0.0
    counter = Counter(text)
    length = len(text)
    entropy = 0.0
    for count in counter.values():
        p = count / length
        if p > 0:
            entropy -= p * math.log2(p)
    return round(entropy, 4)


@dataclass
class JSContext:
    """Shared raw materials for one JS sample, consumed by every JS extractor.

    Cheap attributes (raw_bytes, source) are populated eagerly by the factory.
    Expensive ones (scan, ast) are cached_property — computed on first access
    and reused across every extractor that holds the same context.

    `content_type` is the magika label (e.g. `"javascript"`) carried alongside
    the source so the new code_text_content writer (and any future generic
    text-content writer) can record it without re-running magika. Defaults to
    `"javascript"` because by construction this context type is JS-specific;
    workers.py supplies the actual magika value when it builds the context.
    """

    filepath: str
    raw_bytes: bytes
    source: str
    log: Any = None
    content_type: str = "javascript"
    # Populated by JSStringsExtractor.extract() (the decoded/reconstructed
    # strings — hex/unicode/charcode/base64/concat unpacked into plaintext).
    # Read post-loop by the IOC plumbing in workers.py so any IOCs hidden
    # behind those encodings get scraped from the decoded form. Stays None
    # if JSStringsExtractor didn't run for this sample.
    decoded_strings: Optional[list] = None

    @cached_property
    def lines(self) -> List[str]:
        return self.source.splitlines() if self.source else []

    @cached_property
    def text_entropy(self) -> float:
        return _text_entropy(self.source)

    @cached_property
    def scan(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over self.source."""
        return scan_source(self.source) if self.source else {}

    @cached_property
    def ast(self) -> Optional[Any]:
        """Lazy pyjsparser AST. Returns None if the parser is missing or fails.

        Extractors should treat None AST as "fall back to regex" — every
        AST-consuming extractor already handles that path.
        """
        if not self.source:
            return None
        try:
            import pyjsparser
            return pyjsparser.parse(self.source)
        except ImportError:
            if self.log is not None:
                self.log.debug("pyjsparser not installed, AST analysis skipped")
        except Exception as e:
            if self.log is not None:
                self.log.warning(f"AST parsing failed for {self.filepath}: {e}")
        return None

    @cached_property
    def deobfuscated(self) -> "tuple[Optional[str], Optional[str]]":
        """Run the configured JS deobfuscator (with jsbeautifier fallback) once
        per sample and cache the result. Returns `(text, normalizer_used)` or
        `(None, None)` if neither path produced output.

        Computed lazily on first access — samples whose pipeline never reads
        this don't pay the subprocess cost.
        """
        from redb.extractors.js_extractors.js_deobfuscator import deobfuscate
        return deobfuscate(self.source, self.log)

    @cached_property
    def scan_deobfuscated(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over the deobfuscated
        text, keyed by PATTERNS only (FEATURE_PATTERNS are not consulted by
        the dual-pass consumers). Empty dict when there is no deobfuscated
        text or it equals the raw source.

        Two extractors consume the post-deobf API surface:
        `JSSuspiciousAPIsExtractor` (for revealed_by_deobf rows) and
        `JSDeobfuscationExtractor` (for the new_apis_found diff). Caching here
        means we scan the deobfuscated text once instead of twice per sample.
        """
        from redb.extractors.js_extractors.js_patterns import PATTERNS
        deobf_text, _ = self.deobfuscated
        if not deobf_text or deobf_text == self.source:
            return {}
        return scan_source(deobf_text, patterns=(PATTERNS,))

    @cached_property
    def xray(self):
        """Run @nodesecure/js-x-ray once per sample and cache the result.

        Returns an `XRayResult` (always — the function collapses every failure
        path to an empty result so callers don't have to special-case missing
        Node, missing package, timeouts, or parse errors). The
        `JSFeaturesExtractor` reads it for the obfuscator family name and for
        corroborating warning kinds; the heuristic falls back cleanly when
        `xray.obfuscator is None`.
        """
        from redb.extractors.js_extractors.js_xray import run
        return run(self.source, self.log)

    @classmethod
    def from_path(
        cls,
        filepath: str,
        log: Any = None,
        source: Optional[str] = None,
        raw_bytes: Optional[bytes] = None,
        content_type: str = "javascript",
    ) -> "JSContext":
        """Build a context from disk. `raw_bytes` and `source` are optional
        overrides — useful when the caller has already read or decoded the file.
        `content_type` is the magika label workers.py dispatched on; it lands
        on the context for the code_text_content writer to record.
        """
        if raw_bytes is None:
            with open(filepath, "rb") as f:
                raw_bytes = f.read()
        if source is None:
            source = decode_source(raw_bytes)
        return cls(
            filepath=filepath,
            raw_bytes=raw_bytes,
            source=source,
            log=log,
            content_type=content_type,
        )