Xavier Desquesnes

24 papers B 2Journal 8Unranked 13
YearRankTypeTitle / Venue / Authors
2024 J jnl
J. Imaging
Stuardo Lucho, Sylvie Treuillet, Xavier Desquesnes, Remy Leconge, Xavier Brunetaud
2022 conf
ICIAP Workshops
Souhaieb Aouayeb, Xavier Desquesnes, Bruno Emile, Baptiste Mulot, Sylvie Treuillet
2022 conf
ICIAP Workshops
Koubouratou Idjaton, Xavier Desquesnes, Sylvie Treuillet, Xavier Brunetaud
2020 conf
ICPR Workshops (7)
Koubouratou Idjaton, Xavier Desquesnes, Sylvie Treuillet, Xavier Brunetaud
2019 conf
GbRPR
Kaouther Tabia, Xavier Desquesnes, Yves Lucas, Sylvie Treuillet
2018 conf
WHISPERS
Kaouther Tabia, Xavier Desquesnes, Yves Lucas, Sylvie Treuillet
2017 J jnl
IEEE J. Sel. Top. Signal Process.
Xavier Desquesnes, Abderrahim Elmoataz
2016 B conf
ACIVS
Kaouther Tabia, Xavier Desquesnes, Yves Lucas, Sylvie Treuillet
2016 J jnl
IEEE J. Sel. Top. Signal Process.
Matthieu Toutain, Abderrahim Elmoataz, Xavier Desquesnes, Jean-Hugues Pruvot
2016 conf
VISIGRAPP (4: VISAPP)
Sonia Gharsalli, Bruno Emile, Hélène Laurent, Xavier Desquesnes
2015 conf
IPTA
Sonia Gharsalli, Bruno Emile, Hélène Laurent, Xavier Desquesnes, Damien Vivet
2015 conf
VISAPP (2)
Sonia Gharsalli, Hélène Laurent, Bruno Emile, Xavier Desquesnes
2014 J jnl
Pattern Recognit. Lett.
Sadia Alkama, Xavier Desquesnes, Abderrahim Elmoataz
2014 J jnl
Signal Process.
Abdallah El Chakik, Abderrahim Elmoataz, Xavier Desquesnes
2014 J jnl
Math. Comput. Simul.
Abderrahim Elmoataz, Xavier Desquesnes, Zakaria Lakhdari, Olivier Lézoray
2013 conf
ALCOSP
Matthieu Toutain, Xavier Desquesnes, Abderrahim Elmoataz, Olivier Lézoray
2013 J jnl
J. Math. Imaging Vis.
Xavier Desquesnes, Abderrahim Elmoataz, Olivier Lézoray
2013 conf
ALCOSP
Abdallah El Chakik, Xavier Desquesnes, Abderrahim Elmoataz
2012 conf
ACCV (4)
Abdallah El Chakik, Xavier Desquesnes, Abderrahim Elmoataz
2012 J jnl
IEEE J. Sel. Top. Signal Process.
Abderrahim Elmoataz, Xavier Desquesnes, Olivier Lezoray
2012 conf
ISBI
Xavier Desquesnes, Abderrahim Elmoataz, Olivier Lezoray
2012
Xavier Desquesnes
2011 B conf
ICIP
Xavier Desquesnes, Abderrahim Elmoataz, Olivier Lezoray
2010 conf
ISVC (2)
Xavier Desquesnes, Abderrahim Elmoataz, Olivier Lezoray, Vinh-Thong Ta
redb/extractors/js_extractors/js_suspicious_apis.py
← Index redb/extractors/js_extractors/js_suspicious_apis.py python
import inspect
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 CATEGORIES, PATTERNS


# Backwards-compatible export: `{category: [(raw_pattern_string, api_name), ...]}`
# in canonical PATTERNS insertion order (code_execution, network, filesystem,
# process, registry, crypto_encoding, dom_manipulation). Kept so external
# callers (notably JSDeobfuscationExtractor pre-cleanup) keep working until
# they are migrated to PATTERNS directly.
SUSPICIOUS_APIS: "dict[str, list[tuple[str, str]]]" = {}
for _name, _compiled in PATTERNS.items():
    SUSPICIOUS_APIS.setdefault(CATEGORIES[_name], []).append((_compiled.pattern, _name))


class JSSuspiciousAPIsExtractor(JSExtractor):

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

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

    def _get_context_snippet(self, line, max_len=200):
        """Get a truncated context snippet around a match."""
        line = line.strip()
        if len(line) > max_len:
            return line[:max_len] + "..."
        return line

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

        # Pass 1: shared per-sample scan over the raw source. The dict contains
        # entries for both PATTERNS and FEATURE_PATTERNS; the loop below only
        # consults PATTERNS keys, so feature-only entries are ignored.
        raw_scan = self._context.scan or {}
        raw_lines = self.lines

        # Pass 2: same patterns over the deobfuscated text, when the
        # deobfuscator produced something meaningfully different. APIs hidden
        # behind one obfuscation layer (Vjw0rm-style array.join + eval,
        # Dean-Edwards packers, jjencode, ...) only surface here. The scan is
        # cached on JSContext so JSDeobfuscationExtractor (which computes the
        # new_apis_found diff) reuses the same result.
        deobf_scan = self._context.scan_deobfuscated
        if deobf_scan:
            deobf_text, _ = self._context.deobfuscated
            deobf_lines = deobf_text.splitlines()
        else:
            deobf_lines = []

        findings = []
        # Iterate PATTERNS in canonical order so output is deterministic and
        # matches the historical category/pattern ordering. For each api_name,
        # raw findings take precedence; if an API is found only in the
        # deobfuscated text, we surface it as a row tagged revealed_by_deobf=1
        # with line numbers / snippets pulled from the deobfuscated source.
        for api_name in PATTERNS:
            raw_info = raw_scan.get(api_name)
            if raw_info:
                line_numbers = raw_info["lines"]
                lines_for_snippets = raw_lines
                revealed_by_deobf = 0
            else:
                deobf_info = deobf_scan.get(api_name)
                if not deobf_info:
                    continue
                line_numbers = deobf_info["lines"]
                lines_for_snippets = deobf_lines
                revealed_by_deobf = 1

            snippets = [
                self._get_context_snippet(lines_for_snippets[ln - 1])
                for ln in line_numbers[:3]
                if 0 < ln <= len(lines_for_snippets)
            ]
            findings.append({
                "api_name": api_name,
                "api_category": CATEGORIES[api_name],
                # Historical semantics: count = number of unique lines with a
                # match, not total in-source match count.
                "call_count": len(line_numbers),
                "line_numbers": line_numbers,
                "context_snippet": " | ".join(snippets),
                "revealed_by_deobf": revealed_by_deobf,
            })

        if not findings:
            return None

        self.api_findings = findings
        return findings

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

            current_time = datetime.now(timezone.utc)
            data = []
            for f in self.api_findings:
                data.append([
                    self.sha256,
                    f['api_name'],
                    f['api_category'],
                    f['call_count'],
                    f['line_numbers'],
                    f['context_snippet'],
                    f['revealed_by_deobf'],
                    current_time,
                ])

            column_names = [
                "sha256", "api_name", "api_category",
                "call_count", "line_numbers", "context_snippet",
                "revealed_by_deobf",
                "analysis_date",
            ]

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

            return (data, column_names, column_type_names)

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