Ramdas Kumaresan

56 papers A 2Misc 26Journal 23Unranked 5
YearRankTypeTitle / Venue / Authors
2020 J jnl
SN Comput. Sci.
Vijay Kumar Peddinti, Ramdas Kumaresan, Peter Cariani
2018 J jnl
J. Ambient Intell. Humaniz. Comput.
Harishchandra Dubey, Ramdas Kumaresan, Kunal Mankodiya
2016 J jnl
Signal Process.
Vijay Kumar Peddinti, Ramdas Kumaresan
2016 J jnl
CoRR
Harishchandra Dubey, Ramdas Kumaresan, Kunal Mankodiya
2014 Misc conf
ICASSP
Ramdas Kumaresan, Vijay Kumar Peddinti, Peter Cariani
2012 Misc conf
ICASSP
Ramdas Kumaresan, Vijay Kumar Peddinti, Peter Cariani
2011 Misc conf
ICASSP
Ramdas Kumaresan, Vijay Kumar Peddinti, Peter Cariani
2010 J jnl
IEEE Trans. Speech Audio Process.
Ramdas Kumaresan, Nitesh Panchal
2005 conf
ICASSP (1)
Ramdas Kumaresan, Gopi Krishna Allu, Peter Cariani
2005 A conf
INTERSPEECH
Yadong Wang, Steven Greenberg, Jayaganesh Swaminathan, Ramdas Kumaresan, David Poeppel
2003 A conf
INTERSPEECH
Yadong Wang, Jesse Hansen, Gopi Krishna Allu, Ramdas Kumaresan
2001 J jnl
IEEE Trans. Speech Audio Process.
Ramdas Kumaresan, Yadong Wang
2000 Misc conf
ICASSP
Ramdas Kumaresan, Yadong Wang
2000 J jnl
IEEE Trans. Speech Audio Process.
Ashwin Rao, Ramdas Kumaresan
2000 J jnl
IEEE Trans. Signal Process.
Ramdas Kumaresan, Ashwin Rao
1999 Misc conf
ICASSP
Ramdas Kumaresan
1999 J jnl
IEEE Trans. Signal Process.
Ramdas Kumaresan, Ashwin Rao
1998 J jnl
IEEE Signal Process. Lett.
Ashwin Rao, Ramdas Kumaresan
1998 Misc conf
ICASSP
Ramdas Kumaresan, Ashwin Rao
1998 J jnl
IEEE Signal Process. Lett.
Ramdas Kumaresan
1998 Misc conf
ICASSP
Arnab K. Shaw, Srikanth Pokala, Ramdas Kumaresan
1998 J jnl
Multidimens. Syst. Signal Process.
Ramdas Kumaresan, Ashwin Rao
1996 Misc conf
ICASSP
Xiaoshu Qian, Ramdas Kumaresan
1996 Misc conf
ICASSP
Ashwin Rao, Ramdas Kumaresan
1995 Misc conf
ICASSP
Ashwin Rao, Ramdas Kumaresan
1995 Misc conf
ICASSP
C. S. Ramalingam, Ramdas Kumaresan
1994 conf
ICASSP (2)
Donald W. Tufts, Hongya Ge, Ramdas Kumaresan
1994 conf
ICASSP (1)
C. S. Ramalingam, Ramdas Kumaresan
1993 conf
ICASSP (4)
Ramdas Kumaresan, C. S. Ramalingam
1993 J jnl
IEEE Trans. Signal Process.
Paul M. Baggenstoss, Ramdas Kumaresan
1993 conf
ICASSP (5)
Stephan Hoefer, Frank Heil, Madhukar Pandit, Ramdas Kumaresan
1992 Misc conf
ICASSP
Geoffrey S. Edelson, Ramdas Kumaresan, Donald W. Tufts
1991 Misc conf
ICASSP
Stephan Hoefer, Ramdas Kumaresan, Madhukar Pandit, T. Stollhof
1991 J jnl
IEEE Trans. Signal Process.
Ramdas Kumaresan, Y. Feng
1991 Misc conf
ICASSP
Ramdas Kumaresan, C. Sidney Burrus
1991 Misc conf
ICASSP
C. S. Ramalingam, Ramdas Kumaresan, Dirk van Ormondt
1990 J jnl
IEEE Trans. Acoust. Speech Signal Process.
Ramdas Kumaresan
1989 J jnl
IEEE Trans. Acoust. Speech Signal Process.
A. P. Shenoy, Ramdas Kumaresan
1989 J jnl
IEEE Trans. Computers
A. P. Shenoy, Ramdas Kumaresan
1988 J jnl
IEEE Trans. Acoust. Speech Signal Process.
Prabhat Kumar Gupta, Ramdas Kumaresan
1988 Misc conf
ICASSP
Arnab K. Shaw, Ramdas Kumaresan
1988 J jnl
Neural Networks
Prabhat Kumar Gupta, Ramdas Kumaresan
1987 Misc conf
ICASSP
A. P. Shenoy, Ramdas Kumaresan
1987 Misc conf
ICASSP
R. Rastogi, Prabhat Kumar Gupta, Ramdas Kumaresan
1987 Misc conf
ICASSP
Arnab K. Shaw, Ramdas Kumaresan
1986 J jnl
IEEE Trans. Acoust. Speech Signal Process.
Ramdas Kumaresan, Louis L. Scharf, Arnab K. Shaw
1986 J jnl
Proc. IEEE
Ramdas Kumaresan, Arnab K. Shaw
1986 J jnl
Proc. IEEE
Ramdas Kumaresan, Prabhat Kumar Gupta
1985 J jnl
Proc. IEEE
Ramdas Kumaresan, Prabhat Kumar Gupta
1985 Misc conf
ICASSP
Ramdas Kumaresan
1985 Misc conf
ICASSP
Ramdas Kumaresan, Arnab K. Shaw
1985 Misc conf
ICASSP
Ramdas Kumaresan, Prabhat Kumar Gupta
1984 Misc conf
ICASSP
Donald W. Tufts, Ramdas Kumaresan
1984 Misc conf
ICASSP
M. R. Baraniecki, Ramdas Kumaresan, Anna Z. Baraniecki, Malayappan Shridhar
1982 Misc conf
ICASSP
Ramdas Kumaresan, Donald W. Tufts
1980 Misc conf
ICASSP
Donald W. Tufts, Ramdas Kumaresan
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,
        )