Manuel Carreiras

34 papers A 1B 5Journal 26Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
NeuroImage
Abraham Sánchez, Pedro M. Paz-Alonso, Manuel Carreiras
2025 B conf
CogSci
Suhail Matar, Paloma Morcillo Ortega, Manuel Carreiras
2020 J jnl
NeuroImage
Mathieu Bourguignon, Nicola Molinaro, Mikel Lizarazu, Samu Taulu, Veikko Jousmäki, Marie Lallier, Manuel Carreiras, Xavier De Tiège
2020 B conf
CogSci
Camila Zugarramurdi, Manuel Carreiras, Juan C. Valle-Lisboa
2019 J jnl
NeuroImage
Usman Ayub Sheikh, Manuel Carreiras, David Soto
2019 J jnl
NeuroImage
Gurunandan Krishnan, Manuel Carreiras, Pedro M. Paz-Alonso
2019 conf
Interacción
Nestor Garay-Vitoria, Andoni Arruti, José Ignacio Martín, Javier Muguerza, Ainara Garzo, Erik Hernández, Ainhoa Álvarez, Jon Arambarri, Pedro M. Paz-Alonso, Manuel Carreiras, Ander Ramos-Murguialday
2018 A conf
ASSETS
Mikel Ostiz-Blanco, Marie Lallier, Sergi Grau Carrión, Luz Rello, Jeffrey P. Bigham, Manuel Carreiras
2018 J jnl
NeuroImage
Ileana Quiñones, Nicola Molinaro, Simona Mancini, Juan Andrés Hernández-Cabrera, Horacio A. Barber, Manuel Carreiras
2015 J jnl
J. Cogn. Neurosci.
Wouter De Baene, Wouter Duyck, Marcel Brass, Manuel Carreiras
2015 conf
EMBC
Mattia F. Pagnotta, George Zouridakis, Lianyang Li, Mikel Lizarazu, Marie Lallier, Nicola Molinaro, Manuel Carreiras
2015 J jnl
NeuroImage
Manuel Carreiras, Philip J. Monahan, Mikel Lizarazu, Jon Andoni Duñabeitia, Nicola Molinaro
2015 J jnl
NeuroImage
Manuel Carreiras, Ileana Quiñones, Simona Mancini, Juan Andrés Hernández-Cabrera, Horacio A. Barber
2014 J jnl
NeuroImage
Lorna García-Pentón, Alejandro Pérez Fernández, Yasser Iturria-Medina, Margaret Gillon-Dowens, Manuel Carreiras
2014 J jnl
NeuroImage
Ileana Quiñones, Nicola Molinaro, Simona Mancini, Juan Andrés Hernández-Cabrera, Manuel Carreiras
2013 J jnl
Lang. Linguistics Compass
Simona Mancini, Nicola Molinaro, Manuel Carreiras
2013 B conf
CogSci
Lorna García-Pentón, Alejandro Pérez Fernández, Yasser Iturria-Medina, Manuel Carreiras
2013 B conf
CogSci
Alejandro Pérez Fernández, Margaret Gillon-Dowens, Nicola Molinaro, Yasser Iturria-Medina, Paulo Barraza, Manuel Carreiras
2013 J jnl
NeuroImage
Nicola Molinaro, Horacio A. Barber, Alejandro Pérez Fernández, Lauri Parkkonen, Manuel Carreiras
2013 J jnl
NeuroImage
Nicola Molinaro, Paulo Barraza, Manuel Carreiras
2013 J jnl
J. Cogn. Neurosci.
Manuel Carreiras, Manuel Perea, Cristina Gil-López, Reem Abu Mallouh, Elena Salillas
2012 J jnl
J. Cogn. Neurosci.
Jon Andoni Duñabeitia, Maria Dimitropoulou, Jonathan Grainger, Juan Andrés Hernández, Manuel Carreiras
2012 J jnl
NeuroImage
Andrea E. Martin, Mante S. Nieuwland, Manuel Carreiras
2012 J jnl
NeuroImage
Nicola Molinaro, Manuel Carreiras, Jon Andoni Duñabeitia
2012 B conf
CogSci
Douglas J. Davidson, Adriana Hanulíková, Manuel Carreiras
2011 J jnl
NeuroImage
Niels Janssen, Manuel Carreiras, Horacio A. Barber
2011 J jnl
NeuroImage
Jon Andoni Duñabeitia, Nicola Molinaro, Manuel Carreiras
2010 J jnl
J. Cogn. Neurosci.
Margaret Gillon-Dowens, Marta Vergara, Horacio A. Barber, Manuel Carreiras
2010 J jnl
Lang. Linguistics Compass
Manuel Carreiras
2010 J jnl
NeuroImage
Manuel Carreiras, Lindsay Carr, Horacio A. Barber, Arturo E. Hernandez
2009 J jnl
J. Cogn. Neurosci.
Manuel Carreiras, Margaret Gillon-Dowens, Marta Vergara, Manuel Perea
2007 J jnl
J. Cogn. Neurosci.
Manuel Carreiras, Andrea Mechelli, Adelina Estévez, Cathy J. Price
2005 J jnl
J. Cogn. Neurosci.
Manuel Carreiras, Marta Vergara, Horacio A. Barber
2005 J jnl
J. Cogn. Neurosci.
Horacio A. Barber, Manuel Carreiras
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"