Carmen Banea

32 papers A* 3B 6Journal 6Unranked 16
YearRankTypeTitle / Venue / Authors
2023 J jnl
Comput. Linguistics
Aparna Garimella, Carmen Banea, Rada Mihalcea
2020 B conf
COLING
Aparna Garimella, Carmen Banea, Nabil Hossain, Rada Mihalcea
2020 J jnl
CoRR
Aparna Garimella, Carmen Banea, Nabil Hossain, Rada Mihalcea
2020 conf
AACL/IJCNLP
Laura Biester, Carmen Banea, Rada Mihalcea
2019 conf
SocInfo
MeiXing Dong, David Jurgens, Carmen Banea, Rada Mihalcea
2019 conf
ACL (1)
Aparna Garimella, Carmen Banea, Eduard H. Hovy, Rada Mihalcea
2018 J jnl
Nat. Lang. Eng.
Carmen Banea, Rada Mihalcea
2017 A* conf
EMNLP
Aparna Garimella, Carmen Banea, Rada Mihalcea
2017 B conf
ACII
Vicki Liu, Carmen Banea, Rada Mihalcea
2017 conf
SocInfo (1)
Shibamouli Lahiri, Carmen Banea, Rada Mihalcea
2016 B conf
LREC
Carmen Banea, Xi Chen, Rada Mihalcea
2016 conf
SemEval@NAACL-HLT
Eneko Agirre, Carmen Banea, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, Janyce Wiebe
2015 conf
SemEval@NAACL-HLT
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, Janyce Wiebe
2014 conf
SemEval@COLING
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, Janyce Wiebe
2014 J jnl
Comput. Speech Lang.
Carmen Banea, Rada Mihalcea, Janyce Wiebe
2014 conf
SemEval@COLING
Carmen Banea, Di Chen, Rada Mihalcea, Claire Cardie, Janyce Wiebe
2013 conf
*SEM@NAACL-HLT
Carmen Banea, Yoonjung Choi, Lingjia Deng, Samer Hassan, Michael Mohler, Bishan Yang, Claire Cardie, Rada Mihalcea, Janyce Wiebe
2013 conf
TAC
Carmen Banea, Rada Mihalcea, Yoonjung Choi, Lingjia Deng, Janyce Wiebe, Ozan Irsoy, Detian Shi, Claire Cardie
2013 J jnl
IEEE Trans. Affect. Comput.
Carmen Banea, Rada Mihalcea, Janyce Wiebe
2012 B conf
LREC
Verónica Pérez-Rosas, Carmen Banea, Rada Mihalcea
2012 conf
*SEM@NAACL-HLT
Samer Hassan, Carmen Banea, Rada Mihalcea
2012 conf
ACL (Tutorial Abstracts)
Rada Mihalcea, Carmen Banea, Janyce Wiebe
2012 conf
SemEval@NAACL-HLT
Carmen Banea, Samer Hassan, Michael Mohler, Rada Mihalcea
2011 conf
IWCS
Carmen Banea, Rada Mihalcea
2010 B conf
COLING
Carmen Banea, Rada Mihalcea, Janyce Wiebe
2010 ed.
TextGraphs@ACL
Carmen Banea, Alessandro Moschitti, Swapna Somasundaran, Fabio Massimo Zanzotto
2008 B conf
LREC
Carmen Banea, Rada Mihalcea, Janyce Wiebe
2008 A* conf
EMNLP
Carmen Banea, Rada Mihalcea, Janyce Wiebe, Samer Hassan
2007 A* conf
ACL
Rada Mihalcea, Carmen Banea, Janyce Wiebe
2007 J jnl
Int. J. Semantic Comput.
Samer Hassan, Rada Mihalcea, Carmen Banea
2007 conf
ICSC
Samer Hassan, Rada Mihalcea, Carmen Banea
2007 conf
SemEval@ACL
Samer Hassan, Andras Csomai, Carmen Banea, Ravi Som Sinha, Rada Mihalcea
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,
        )