Hani Attar

39 papers Journal 26Unranked 13
YearRankTypeTitle / Venue / Authors
2026 J jnl
Int. J. Crowd Sci.
Hani Attar, Haitham Issa, Jafar Ababneh, Khosro Rezaee, Ayat Alrosan, Mohanad A. Deif, Ahmed A. A. Solyman
2025 conf
ICMRE
Hani Attar, Jafar Ababneh, Mohamed Hafez, Ali Rachini, Ahmed A. A. Solyman, Ramy M. Bahy
2025 J jnl
IEEE Access
Yousef O. Sharrab, Hani Attar, Mohammad Ali H. Eljinini, Yasmin Al-Omary, Ammar Ali Almomani
2025 J jnl
Int. J. Crowd Sci.
Hani Attar, Mohammed Alghanim, Jafar Ababneh, Khosro Rezaee, Ayat Alrosan, Mohanad A. Deif
2025 conf
ICMRE
Hani Attar, Yousef O. Sharrab, Saja Smadi, Safa Jbaily
2025 J jnl
IEEE Access
Jafar Ababneh, Majed Mohammed Alnemari, Hani Attar, Hani A. Alghamdi, Muath Al-Shawaheen, Balqies Saqarat, Laila Abu Romman, Musab Iqtait
2025 J jnl
Int. J. Crowd Sci.
Amer Tahseen Abu-Jassar, Hani Attar, Ayman Amer, Vyacheslav V. Lyashenko, Vladyslav Yevsieiev, Ahmed A. A. Solyman
2025 J jnl
CoRR
Arman Mohammadigilani, Hani Attar, Hamidreza Ehsani Chimeh, Mostafa Karami
2025 J jnl
EURASIP J. Wirel. Commun. Netw.
Jafar Ababneh, Hani Attar, Zakaria Che Muda, Ilhami Colak, Mohanad A. Deif, Samir Bendoukha, Ahmed A. A. Solyman
2025 conf
ICMRE
Mohanad A. Deif, Mohamed A. Hafez, Hani Attar, Sohair F. Rezeka, T. Awad, E. M. Attia, M. B. Badawi
2025 J jnl
IEEE Trans. Consumer Electron.
Zhiqing Bai, Xuan Yang, Junzhi Xiang, Hani Attar, Lingyun Lu, Mohamadreza Khosravi, Hailin Qin, Xiaolong Zhao, Zongjiu Zhu, Dejuan Li
2025 J jnl
Intell. Syst. Appl.
Mohanad A. Deif, Hani Attar, Mohammad Aljaidi, Ayoub Alsarhan, Dimah Al-Fraihat, Ahmed A. A. Solyman
2025 J jnl
Appl. Comput. Intell. Soft Comput.
Mohanad A. Deif, Hani Attar, Waleed Alomoush, Mohamed A. Hafez
2025 J jnl
Int. J. Crowd Sci.
Amer Tahseen Abu-Jassar, Hani Attar, Ayman Amer, Vyacheslav V. Lyashenko, Vladyslav Yevsieiev, Ahmed A. A. Solyman
2024 J jnl
Int. J. Intell. Syst.
Mohammed Alghanim, Hani Attar, Khosro Rezaee, Mohammad Reza Khosravi, Ahmed A. A. Solyman, Mohammad A. Kanan
2024 J jnl
Wirel. Networks
Hani Attar, Reza Khosravi, Jafar Ababneh, Ayman Amer, Ahmed A. A. Solyman
2024 conf
ACIT
Hani Attar, Jafar Ababneh, Mohamed A. Hafez, Waleed Alomoush, Hussein Al-Faiz, Mohanad A. Deif
2024 J jnl
IEEE Access
Mariam Essam, Mohanad A. Deif, Hani Attar, Ayat Alrosan, Mohammad A. Kanan, Rania Elgohary
2024 J jnl
PeerJ Comput. Sci.
Guogang Xie, Hani Attar, Ayat Alrosan, Sally Mohammed Farghaly Abdelaliem, Amany Anwar Saeed Alabdullah, Mohanad A. Deif
2024 conf
ACIT
Ahmed Samir, Samir Bendoukha, Hani Attar, Jafar Ababneh, Mohammad Alhihi, Mohamed Hafez
2024 conf
ACIT
Ziad Algendi, Hani Attar, Mohamed A. Hafez, Hussein Al-Faiz, Jafar Ababneh, Mohanad A. Deif
2024 conf
ACIT
Ramy M. Bahy, Jafar Ababneh, Hani Attar, Mohammad Alhihi, Mohamed Hafez, Ahmed A. A. Solyman
2024 conf
ACIT
Hani Attar, Jafar Ababneh, Ali Rachini, Ramy M. Bahy, Mohamed Hafez, Ahmed A. A. Solyman
2024 conf
ICT
Hani Attar, Ramy M. Bahy, Ala' F. Khalifeh, Mohamed Hafez, Ahmed A. A. Solyman
2024 J jnl
Multim. Tools Appl.
Hani Attar, Tasneem Ahmed, Rahma Rabie, Ayman Amer, Mohammad Reza Khosravi, Ahmed A. A. Solyman, Mohanad A. Deif
2024 conf
ACIT
Mennatallah Sherif, Hani Attar, Mohamed A. Hafez, Jafar Ababneh, Hussein Al-Faiz, Mohanad A. Deif
2024 conf
ACIT
Hani Attar, Jafar Ababneh, Mohammad Al-Hihi, Ali Rachini, Ahmed A. A. Solyman
2023 J jnl
Syst.
Qais Ibrahim Ahmed, Hani Attar, Ayman Amer, Mohanad A. Deif, Ahmed A. A. Solyman
2023 J jnl
IEEE Internet Things J.
Khosro Rezaee, Mohammad Reza Khosravi, Hani Attar, Varun G. Menon, Mohammad Ayoub Khan, Haitham Issa, Lianyong Qi
2023 J jnl
ACM J. Data Inf. Qual.
Hani Attar
2023 J jnl
Intell. Autom. Soft Comput.
Ayman Amer, Firas M. Makahleh, Jafar Ababneh, Hani Attar, Ahmed A. A. Solyman, Mehrdad Ahmadi Kamarposhti, Phatiphat Thounthong
2023 J jnl
J. Cloud Comput.
Sahand Hamzehei, Omid Akbarzadeh, Hani Attar, Khosro Rezaee, Nazanin Fasihihour, Mohammad Reza Khosravi
2023 J jnl
ACM J. Data Inf. Qual.
Ahmad Al-Qerem, Ali Mohd Ali, Hani Attar, Shadi Nashwan, Lianyong Qi, Mohammad Kazem Moghimi, Ahmed A. A. Solyman
2022 J jnl
Sensors
Waleed Alomoush, Osama Ahmed Khashan, Ayat Alrosan, Essam H. Houssein, Hani Attar, Mohammed Alweshah, Fuad Alhosban
2022 J jnl
Comput. Intell. Neurosci.
Hani Attar, Amer Tahseen Abu-Jassar, Vladyslav Yevsieiev, Vyacheslav V. Lyashenko, Igor Nevliudov, Ashish Kr. Luhach
2014 conf
DICTAP
Hani Attar, Lina Stankovic, Mohamed Alhihi, Ahmed Ameen
2013 J jnl
IEEE J. Sel. Areas Commun.
Sajid Nazir, Vladimir Stankovic, Hani Attar, Lina Stankovic, Samuel Cheng
2012 J jnl
IET Commun.
Hani Attar, Lina Stankovic, Vladimir Stankovic
2011 conf
NTMS
Hani Attar, Dejan Vukobratovic, Lina Stankovic, Vladimir Stankovic
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,
        )