Jan G. Bjaalie

26 papers Journal 25
YearRankTypeTitle / Venue / Authors
2025 J jnl
Frontiers Neuroinformatics
Maja A. Puchades, Sharon C. Yates, Gergely Csúcs, Harry Carey, Arda Balkir, Trygve B. Leergaard, Jan G. Bjaalie
2024 J jnl
Frontiers Neuroinformatics
Camilla H. Blixhavn, Ingrid Reiten, Heidi Kleven, Martin Øvsthus, Sharon C. Yates, Ulrike Schlegel, Maja A. Puchades, Oliver Schmid, Jan G. Bjaalie, Ingvild Elise Bjerke, Trygve B. Leergaard
2023 J jnl
Frontiers Neuroinformatics
Heidi Kleven, Ingrid Reiten, Camilla H. Blixhavn, Ulrike Schlegel, Martin Øvsthus, Eszter A. Papp, Maja A. Puchades, Jan G. Bjaalie, Trygve B. Leergaard, Ingvild Elise Bjerke
2022 J jnl
Neuroinformatics
Mathew Birdsall Abrams, Jan G. Bjaalie, Samir Das, Gary F. Egan, Satrajit S. Ghosh, Wojtek James Goscinski, Jeffrey S. Grethe, Jeanette Hellgren Kotaleski, Eric Tatt Wei Ho, David N. Kennedy, Linda J. Lanyon, Trygve B. Leergaard, Helen S. Mayberg, Luciano Milanesi, Roman Moucek, Jean-Baptiste Poline, Prasun Roy, Stephen C. Strother, Tong Boon Tang, Paul H. E. Tiesinga, Thomas Wachtler, Daniel K. Wójcik, Maryann E. Martone
2022 J jnl
NeuroImage
Michael Schirner, Lia Domide, Dionysios Perdikis, Paul Triebkorn, Leon Stefanovski, Roopa Pai, Paula Prodan, Bogdan Valean, Jessica Palmer, Chloê Langford, André Blickensdörfer, Michiel van der Vlag, Sandra Diaz-Pier, Alexander Peyser, Wouter Klijn, Dirk Pleiter, Anne Nahm, Oliver Schmid, Michael Marmaduke Woodman, Lyuba Zehl, Jan Fousek, Spase Petkoski, Lionel Kusch, Meysam Hashemi, Daniele Marinazzo, Jean-François Mangin, Agnes Flöel, Simisola Akintoye, Bernd Carsten Stahl, Michael Cepic, Emily Johnson, Gustavo Deco, Anthony R. McIntosh, Claus C. Hilgetag, Marc Morgan, Bernd Schuller, Alex Upton, Colin McMurtrie, Timo Dickscheid, Jan G. Bjaalie, Katrin Amunts, Jochen Mersmann, Viktor K. Jirsa, Petra Ritter
2022 J jnl
Neuroinformatics
Mathew Birdsall Abrams, Jan G. Bjaalie, Samir Das, Gary F. Egan, Satrajit S. Ghosh, Wojtek James Goscinski, Jeffrey S. Grethe, Jeanette Hellgren Kotaleski, Eric Tatt Wei Ho, David N. Kennedy, Linda J. Lanyon, Trygve B. Leergaard, Helen S. Mayberg, Luciano Milanesi, Roman Moucek, Jean-Baptiste Poline, Prasun Roy, Stephen C. Strother, Tong Boon Tang, Paul H. E. Tiesinga, Thomas Wachtler, Daniel K. Wójcik, Maryann E. Martone
2022 J jnl
CoRR
Malin Sandström, Mathew Birdsall Abrams, Jan G. Bjaalie, Mona Hicks, David N. Kennedy, Arvind Kumar, Jean-Baptiste Poline, Prasun Roy, Paul H. E. Tiesinga, Thomas Wachtler, Wojtek Goscinski
2021 J jnl
CoRR
Michael Schirner, Lia Domide, Dionysios Perdikis, Paul Triebkorn, Leon Stefanovski, Roopa Pai, Paula Popa, Bogdan Valean, Jessica Palmer, Chloê Langford, André Blickensdörfer, Michiel van der Vlag, Sandra Diaz-Pier, Alexander Peyser, Wouter Klijn, Dirk Pleiter, Anne Nahm, Oliver Schmid, Michael Marmaduke Woodman, Lyuba Zehl, Jan Fousek, Spase Petkoski, Lionel Kusch, Meysam Hashemi, Daniele Marinazzo, Jean-François Mangin, Agnes Flöel, Simisola Akintoye, Bernd Carsten Stahl, Michael Cepic, Emily Johnson, Anthony R. McIntosh, Claus C. Hilgetag, Marc Morgan, Bernd Schuller, Alex Upton, Colin McMurtrie, Timo Dickscheid, Jan G. Bjaalie, Katrin Amunts, Jochen Mersmann, Viktor K. Jirsa, Petra Ritter
2021 J jnl
Frontiers Neuroinformatics
Michel Dojat, Jan G. Bjaalie, Emmanuel Luc Barbier
2020 J jnl
Frontiers Neuroinformatics
Nicolaas E. Groeneboom, Sharon C. Yates, Maja A. Puchades, Jan G. Bjaalie
2019 J jnl
Frontiers Neuroinformatics
Sharon C. Yates, Nicolaas E. Groeneboom, Christopher Coello, Stefan F. Lichtenthaler, Peer-Hendrik Kuhn, Hans-Ulrich Demuth, Maike Hartlage-Rübsamen, Steffen Roßner, Trygve B. Leergaard, Anna Kreshuk, Maja A. Puchades, Jan G. Bjaalie
2016 J jnl
Frontiers Neuroinformatics
Eszter A. Papp, Trygve B. Leergaard, Gergely Csúcs, Jan G. Bjaalie
2015 J jnl
NeuroImage
Eszter A. Papp, Trygve B. Leergaard, Evan Calabrese, G. Allan Johnson, Jan G. Bjaalie
2015 J jnl
NeuroImage
Lisa J. Kjonigsen, Sveinung Lillehaug, Jan G. Bjaalie, Menno P. Witter, Trygve B. Leergaard
2014 J jnl
Frontiers Neuroinformatics
Izabela M. Zakiewicz, Jan G. Bjaalie, Trygve B. Leergaard
2014 J jnl
NeuroImage
Katrin Amunts, Michael Hawrylycz, David C. Van Essen, John D. Van Horn, Noam Harel, Jean-Baptiste Poline, Federico De Martino, Jan G. Bjaalie, Ghislaine Dehaene-Lambertz, Stanislas Dehaene, Pedro A. Valdés-Sosa, Bertrand Thirion, Karl Zilles, Sean L. Hill, Mathew Birdsall Abrams, Peter A. Tass, Wim Vanduffel, Alan C. Evans, Simon B. Eickhoff
2014 J jnl
NeuroImage
Eszter A. Papp, Trygve B. Leergaard, Evan Calabrese, G. Allan Johnson, Jan G. Bjaalie
2011 J jnl
NeuroImage
Francis Odeh, Trygve B. Leergaard, Jana Boy, Thorsten Schmidt, Olaf Riess, Jan G. Bjaalie
2011 J jnl
Frontiers Neuroinformatics
Lisa J. Kjonigsen, Trygve B. Leergaard, Menno Witter, Jan G. Bjaalie
2008 J jnl
Neural Networks
Jan G. Bjaalie, Sten Grillner, Shiro Usui
2007 ch.
Geometric Modelling, Numerical Simulation, and Optimization
Jens Olav Nygaard, Jan G. Bjaalie, Simen Gaure, Christian Pettersen, Helge Avlesen
2007 J jnl
Frontiers Neuroinformatics
Trine Hjornevik, Trygve B. Leergaard, Dmitri Darine, Olve Moldestad, Anders M. Dale, Frode Willoch, Jan G. Bjaalie
2007 J jnl
Neuroinformatics
Ivar A. Moene, Shankar Subramaniam, Dmitri Darine, Trygve B. Leergaard, Jan G. Bjaalie
2006 J jnl
NeuroImage
Jana Boy, Trygve B. Leergaard, Thorsten Schmidt, Francis Odeh, Ulrike Bichelmeier, Silke Nuber, Carsten Holzmann, Andreas Wree, Stanley B. Prusiner, Hermann Bujard, Olaf Riess, Jan G. Bjaalie
2003 J jnl
NeuroImage
Trygve B. Leergaard, Jan G. Bjaalie, Anna Devor, Lawrence L. Wald, Anders M. Dale
2003 J jnl
Neuroinformatics
Peter Eckersley, Gary F. Egan, Erik De Schutter, Yiyuan Tang, Mirko Novak, Václav Sebesta, Line Matthiessen, Iiro P. Jääskeläinen, Ulla Ruotsalainen, Andreas V. M. Herz, K. Peter Hoffmann, Raphael Ritz, Viji Ravindranath, Francesco Beltrame, Shun-ichi Amari, Shiro Usui, Soo-Young Lee, Jaap van Pelt, Jan G. Bjaalie, Andrzej Wróbel, Fernando Mira da Silva, Carmen González, Sten Grillner, Paul F. M. J. Verschure, Turgay Dalkara, Robert Bennett, David Willshaw, Stephen H. Koslow, Perry L. Miller, Shankar Subramaniam, Arthur W. Toga
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"