Ignace Loris

12 papers Journal 12
YearRankTypeTitle / Venue / Authors
2025 J jnl
J. Comput. Appl. Math.
Ignace Loris, Simone Rebegoldi
2023 J jnl
CoRR
Ignace Loris, Simone Rebegoldi
2022 J jnl
CoRR
Jean-Baptiste Fest, Tommi Heikkilä, Ignace Loris, Ségolène Martin, Luca Ratti, Simone Rebegoldi, Gesa Sarnighausen
2020 J jnl
CoRR
Laurence Denneulin, Nelly Pustelnik, Maud Langlois, Ignace Loris, Éric Thiébaut
2019 J jnl
Numer. Algorithms
Jixin Chen, Ignace Loris
2016 J jnl
SIAM J. Optim.
Silvia Bonettini, Ignace Loris, Federica Porta, Marco Prato
2015 J jnl
Appl. Math. Comput.
Federica Porta, Ignace Loris
2014 J jnl
Comput. Stat.
Vahid Nassiri, Ignace Loris
2014 J jnl
CoRR
Laurent Jacques, Christophe De Vleeschouwer, Yannick Boursier, Prasad Sudhakar, C. De Mol, Aleksandra Pizurica, Sandrine Anthoine, Pierre Vandergheynst, Pascal Frossard, Cagdas Bilen, Srdan Kitic, Nancy Bertin, Rémi Gribonval, Nicolas Boumal, Bamdev Mishra, Pierre-Antoine Absil, Rodolphe Sepulchre, Shaun Bundervoet, Colas Schretter, Ann Dooms, Peter Schelkens, Olivier Chabiron, François Malgouyres, Jean-Yves Tourneret, Nicolas Dobigeon, Pierre Chainais, Cédric Richard, Bruno Cornelis, Ingrid Daubechies, David B. Dunson, Marie Danková, Pavel Rajmic, Kévin Degraux, Valerio Cambareri, Bert Geelen, Gauthier Lafruit, Gianluca Setti, Jean-François Determe, Jérôme Louveaux, François Horlin, Angélique Drémeau, Patrick Héas, Cédric Herzet, Vincent Duval, Gabriel Peyré, Alhussein Fawzi, Mike E. Davies, Nicolas Gillis, Stephen A. Vavasis, Charles Soussen, Luc Le Magoarou, Jingwei Liang, Jalal Fadili, Antoine Liutkus, David Martina, Sylvain Gigan, Laurent Daudet, Mauro Maggioni, Stanislav Minsker, Nate Strawn, C. Mory, Fred Maurice Ngolè Mboula, Jean-Luc Starck, Ignace Loris, Samuel Vaiter, Mohammad Golbabaee, Dejan Vukobratovic
2013 J jnl
Comput. Optim. Appl.
Ignace Loris, Caroline Verhoeven
2010 J jnl
J. Comput. Phys.
Ignace Loris, H. Douma, G. Nolet, Ingrid Daubechies, C. Regone
2008 J jnl
Comput. Phys. Commun.
Ignace Loris
redb/extractors/js_extractors/js_deobfuscation.py
← Index redb/extractors/js_extractors/js_deobfuscation.py python
import hashlib
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 PATTERNS

# String literals of 4+ characters; only used by the deobfuscation diff to count
# strings revealed after deobfuscation. Compiled once at module load.
_STRING_LITERAL_4PLUS_RE = re.compile(r"[\"\']([^\"\']{4,})[\"\']")


class JSDeobfuscationExtractor(JSExtractor):
    """Compute pre/post-deobfuscation metrics for a JS sample.

    The actual deobfuscation pass (external tool with jsbeautifier fallback)
    lives on `JSContext.deobfuscated` and is cached per sample, so any other
    extractor that needs the deobfuscated text reads the same value without
    re-running the subprocess. Configure the external tool via env vars:
        JS_DEOBFUSCATOR_PATH    Path or name (default: webcrack)
        JS_DEOBFUSCATE_TIMEOUT  Seconds (default: 60)
    """

    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.deobfuscation_result = None
        self.log.debug(inspect.currentframe().f_code.co_name)

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

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

        deobfuscated, deobfuscator_used = self._context.deobfuscated
        if deobfuscated is None:
            return None

        original_size = len(src)
        original_entropy = self._context.text_entropy
        deobfuscated_size = len(deobfuscated)
        deobfuscated_entropy = self._calculate_text_entropy(deobfuscated)
        size_change_ratio = round(deobfuscated_size / original_size, 4) if original_size else 0.0

        # Strings revealed by deobfuscation: matched literals are extracted from
        # both versions and the set difference is the count of "new" strings.
        original_strings = set(_STRING_LITERAL_4PLUS_RE.findall(src))
        deobfuscated_strings = set(_STRING_LITERAL_4PLUS_RE.findall(deobfuscated))
        new_strings = deobfuscated_strings - original_strings

        # Suspicious APIs revealed by deobfuscation. Both sides of the diff
        # come from JSContext caches: the raw scan is computed once for the
        # whole pipeline; the deobfuscated scan is computed once and reused
        # by JSSuspiciousAPIsExtractor's revealed_by_deobf rows.
        original_apis = {n for n in self._context.scan if n in PATTERNS}
        deobfuscated_apis = set(self._context.scan_deobfuscated)
        new_apis = deobfuscated_apis - original_apis

        deobfuscated_sha256 = hashlib.sha256(deobfuscated.encode('utf-8')).hexdigest()

        self.deobfuscation_result = {
            'deobfuscator_used': deobfuscator_used,
            'deobfuscation_successful': True,
            'original_size': original_size,
            'deobfuscated_size': deobfuscated_size,
            'size_change_ratio': size_change_ratio,
            'original_entropy': original_entropy,
            'deobfuscated_entropy': deobfuscated_entropy,
            'new_strings_found': len(new_strings),
            'new_apis_found': len(new_apis),
            'deobfuscated_sha256': deobfuscated_sha256,
        }
        return self.deobfuscation_result

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

            r = self.deobfuscation_result
            current_time = datetime.now(timezone.utc)
            data = [[
                self.sha256,
                r['deobfuscator_used'],
                int(r['deobfuscation_successful']),
                r['original_size'],
                r['deobfuscated_size'],
                r['size_change_ratio'],
                r['original_entropy'],
                r['deobfuscated_entropy'],
                r['new_strings_found'],
                r['new_apis_found'],
                r['deobfuscated_sha256'],
                current_time,
            ]]

            column_names = [
                "sha256", "deobfuscator_used", "deobfuscation_successful",
                "original_size", "deobfuscated_size", "size_change_ratio",
                "original_entropy", "deobfuscated_entropy",
                "new_strings_found", "new_apis_found",
                "deobfuscated_sha256", "analysis_date",
            ]

            column_type_names = [
                "FixedString(64)", "LowCardinality(String)", "UInt8",
                "UInt64", "UInt64", "Float64",
                "Float64", "Float64",
                "UInt32", "UInt32",
                "FixedString(64)", "DateTime64(3, 'UTC')",
            ]

            return (data, column_names, column_type_names)

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