Maciej Antczak

26 papers Journal 24Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
Int. J. Appl. Math. Comput. Sci.
Sylwester Swat, Maciej Antczak, Tomasz Zok, Jacek Blazewicz, Jedrzej Musial
2025 J jnl
Bioinform.
Marek Justyna, Craig L. Zirbel, Maciej Antczak, Marta Szachniuk
2025 J jnl
Bioinform.
Jan Pielesiak, Maciej Antczak, Marta Szachniuk, Tomasz Zok
2024 J jnl
PLoS Comput. Biol.
Bartosz Ambrozy Gren, Maciej Antczak, Tomasz Zok, Joanna I. Sulkowska, Marta Szachniuk
2023 J jnl
Bioinform.
Michal Zurkowski, Maciej Antczak, Marta Szachniuk
2023 J jnl
Briefings Bioinform.
Marek Justyna, Maciej Antczak, Marta Szachniuk
2022 J jnl
Bioinform.
Jakub Wiedemann, Jacek Kaczor, Maciej Milostan, Tomasz Zok, Jacek Blazewicz, Marta Szachniuk, Maciej Antczak
2022 J jnl
Bioinform.
Bartosz Adamczyk, Maciej Antczak, Marta Szachniuk
2022 J jnl
Nucleic Acids Res.
Kamil Luwanski, Vladyslav Hlushchenko, Mariusz Popenda, Tomasz Zok, Joanna Sarzynska, Daniil Martsich, Marta Szachniuk, Maciej Antczak
2021 J jnl
CoRR
Jan Badura, Artur Laskowski, Maciej Antczak, Jacek Blazewicz, Grzegorz Pawlak, Erwin Pesch, Thomas Villmann, Szymon Wasik
2020 J jnl
Int. J. Appl. Math. Comput. Sci.
Tomasz Zok, Jan Badura, Sylwester Swat, Kacper Figurski, Mariusz Popenda, Maciej Antczak
2019 J jnl
Bioinform.
Maciej Antczak, Marcin Zablocki, Tomasz Zok, Agnieszka Rybarczyk, Jacek Blazewicz, Marta Szachniuk
2018 J jnl
ACM Comput. Surv.
Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, Tomasz Sternal
2018 J jnl
CoRR
Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski
2018 J jnl
Bioinform.
Maciej Antczak, Mariusz Popenda, Tomasz Zok, Michal Zurkowski, Ryszard W. Adamiak, Marta Szachniuk
2018 J jnl
BMC Bioinform.
Maciej Antczak, Tomasz Zok, Maciej Osowiecki, Mariusz Popenda, Ryszard W. Adamiak, Marta Szachniuk
2018 J jnl
Nucleic Acids Res.
Tomasz Zok, Maciej Antczak, Michal Zurkowski, Mariusz Popenda, Jacek Blazewicz, Ryszard W. Adamiak, Marta Szachniuk
2017 J jnl
CoRR
Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, Tomasz Sternal
2016 J jnl
RAIRO Oper. Res.
Marek Chlopkowski, Maciej Antczak, Michal Slusarczyk, Aleksander Wdowinski, Michal Zajaczkowski, Marta Kasprzak
2016 conf
CSCW Companion
Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, Tomasz Sternal
2016 J jnl
BMC Bioinform.
Maciej Antczak, Marta Kasprzak, Piotr Lukasiak, Jacek Blazewicz
2015 J jnl
Int. J. Appl. Math. Comput. Sci.
Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz, Marta Szachniuk
2015 J jnl
BMC Bioinform.
Agnieszka Rybarczyk, Natalia Szostak, Maciej Antczak, Tomasz Zok, Mariusz Popenda, Ryszard W. Adamiak, Jacek Blazewicz, Marta Szachniuk
2015 J jnl
Nucleic Acids Res.
Piotr Lukasiak, Maciej Antczak, Tomasz Ratajczak, Marta Szachniuk, Mariusz Popenda, Ryszard W. Adamiak, Jacek Blazewicz
2015 conf
BIBM
Piotr Lukasiak, Maciej Antczak, Tomasz Ratajczak, Jacek Blazewicz
2014 J jnl
Nucleic Acids Res.
Maciej Antczak, Tomasz Zok, Mariusz Popenda, Piotr Lukasiak, Ryszard W. Adamiak, Jacek Blazewicz, Marta Szachniuk
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"