Olena Kovalenko

20 papers Misc 5Unranked 15
YearRankTypeTitle / Venue / Authors
2024 conf
ACIT
Yevhen Palamarchuk, Olena Kovalenko
2024 Misc conf
CSIT
Yevhen Palamarchuk, Olena Kovalenko, Dmytro Pylypenko
2023 Misc conf
ICTERI
Tatyana Ponomarenko, Olena Kovalenko, Tetiana Shynkar, Larysa Harashchenko, Tetiana Holovatenko
2023 Misc conf
ICTERI
Larysa Harashchenko, Olena Kovalenko, Liudmyla Kozak, Olena Litichenko, Dana Sopova
2022 Misc conf
CSIT
Yevhen Palamarchuk, Nataliia Zamkova, Ruslan M. Novytsky, Olena Kovalenko
2022 conf
ICL (2)
Olena Kovalenko, Juergen Koeberlein-Kerler, Nataliia Briukhanova, Nataliia Korolova, Nataliia Bozhko, Olha Lytvyn
2022 conf
ICL (2)
Olena Kovalenko, Tetiana Bondarenko, Evhenyi Hromov, Luís Cardoso, Hennadii Zelenin
2021 conf
CSIT (2)
Olena Kovalenko, Yevhen Palamarchuk, Rymma Yatskovska
2021 conf
ICL (2)
Olena Kovalenko, Liudmyla Shtefan, Tatjana Yaschun, Tetiana Bondarenko, Kyrylo Ohdanskyi
2021 conf
ICL (2)
Olena Kovalenko, Juergen Koeberlein-Kerler, Nataliia Bozhko, Tatjana Yaschun, Tetiana Bondarenko
2021 conf
ICL (2)
Olena Kovalenko, Juergen Koeberlein-Kerler, Nataliia Briukhanova, Nataliia Korolova, Olha Lytvyn
2020 Misc conf
ICTERI
Tetiana Shynkar, Anna Bielienka, Olena Kovalenko
2020 conf
ACIT
Mykhaylo Voynarenko, Larysa Lazebnyk, Viktoriya Hurochkina, Olena Kovalenko, Olena Menchynska
2020 conf
ICTES
Yevhen Palamarchuk, Olena Kovalenko
2019 conf
ICL (2)
Olena Kovalenko, Tetiana Bondarenko, Oleksandr Kupriyanov, Iryna Khotchenko
2019 conf
ICL (2)
Olena Kovalenko, Nataliia Briukhanova, Tetiana Bondarenko, Tatjana Yaschun
2019 conf
CSIT (3)
Oleg Bisikalo, Olena Kovalenko, Yevhen Palamarchuk
2018 conf
CSIT (1)
Olena Kovalenko, Yevhen Palamarchuk
2018 conf
TSP
Vladyslav C. Usenko, Olena Kovalenko, Radim Filip
2018 conf
ICL (2)
Olena Kovalenko, Tetiana Bondarenko, Denys Kovalenko
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"