Casey Paquola

11 papers Journal 11
YearRankTypeTitle / Venue / Authors
2024 J jnl
NeuroImage
Yeongjun Park, Mi Ji Lee, Seulki Yoo, Chae Yeon Kim, Jong Young Namgung, Yunseo Park, Hyunjin Park, Eun-Chong Lee, Yeo Dong Yoon, Casey Paquola, Boris C. Bernhardt, Bo-yong Park
2024 J jnl
Neuroinformatics
Jessica Royer, Casey Paquola, Sofie L. Valk, Matthias Kirschner, Seok-Jun Hong, Bo-yong Park, Richard A. I. Bethlehem, Robert Leech, B. T. Thomas Yeo, Elizabeth Jefferies, Jonathan Smallwood, Daniel S. Margulies, Boris C. Bernhardt
2023 J jnl
NeuroImage
Sara Larivière, Seyma Bayrak, Reinder Vos de Wael, Oualid M. Benkarim, Peer Herholz, Raúl Rodríguez-Cruces, Casey Paquola, Seok-Jun Hong, Bratislav Misic, Alan C. Evans, Sofie L. Valk, Boris C. Bernhardt
2022 J jnl
NeuroImage
Oualid M. Benkarim, Casey Paquola, Bo-yong Park, Jessica Royer, Raúl Rodríguez-Cruces, Reinder Vos de Wael, Bratislav Misic, Gemma Piella, Boris C. Bernhardt
2022 J jnl
NeuroImage
Raúl Rodríguez-Cruces, Jessica Royer, Peer Herholz, Sara Larivière, Reinder Vos de Wael, Casey Paquola, Oualid M. Benkarim, Bo-yong Park, Janie Degré-Pelletier, Mark C. Nelson, Jordan DeKraker, Ilana R. Leppert, Christine L. Tardif, Jean-Baptiste Poline, Luis Concha, Boris C. Bernhardt
2021 J jnl
NeuroImage
Nathan Cross, Casey Paquola, Florence B. Pomares, Aurore A. Perrault, Aude Jegou, Alex Nguyen, Ümit Aydin, Boris C. Bernhardt, Christophe Grova, Thien Thanh Dang-Vu
2021 J jnl
NeuroImage
Bo-yong Park, Reinder Vos de Wael, Casey Paquola, Sara Larivière, Oualid M. Benkarim, Jessica Royer, Shahin Tavakol, Raúl Rodríguez-Cruces, Qiongling Li, Sofie L. Valk, Daniel S. Margulies, Bratislav Misic, Danilo Bzdok, Jonathan Smallwood, Boris C. Bernhardt
2020 J jnl
NeuroImage
Richard A. I. Bethlehem, Casey Paquola, Jakob Seidlitz, Lisa Ronan, Boris C. Bernhardt, Cam-CAN Consortium, Kamen A. Tsvetanov
2020 J jnl
NeuroImage
Jessica Royer, Casey Paquola, Sara Larivière, Reinder Vos de Wael, Shahin Tavakol, Alexander J. Lowe, Oualid M. Benkarim, Alan C. Evans, Danilo Bzdok, Jonathan Smallwood, Birgit Frauscher, Boris C. Bernhardt
2019 J jnl
Brain Connect.
Sara Larivière, Reinder Vos de Wael, Casey Paquola, Seok-Jun Hong, Bratislav Misic, Neda Bernasconi, Andrea Bernasconi, Leonardo Bonilha, Boris C. Bernhardt
2018 J jnl
Brain Connect.
Casey Paquola, Max Bennett, Jim Lagopoulos
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"