Halyna Klym

21 papers Misc 4Journal 3Unranked 14
YearRankTypeTitle / Venue / Authors
2025 conf
MIXDES
Yevhen Bershchanskyi, Halyna Klym
2025 J jnl
SN Comput. Sci.
Oleh Chaplia, Halyna Klym, Marina Konuhova, Anatoli I. Popov
2025 conf
MIXDES
Roman Diachok, Halyna Klym
2025 J jnl
Balt. J. Mod. Comput.
Oleh Chaplia, Halyna Klym
2024 Misc conf
CSIT
Oleh Chaplia, Halyna Klym
2023 conf
DESSERT
Ivan Rudavskyi, Oleksandr Stepanov, Halyna Klym
2023 Misc conf
CSIT
Ivan Rudavskyi, Oleksandr Stepanov, Halyna Klym
2023 conf
DESSERT
Oleh Chaplia, Halyna Klym
2023 conf
IDAACS
Oleh Chaplia, Halyna Klym
2023 conf
DESSERT
Yevhen Bershchanskyi, Halyna Klym
2023 conf
IDAACS
Ivan Rudavskyi, Halyna Klym
2023 Misc conf
CSIT
Oleh Chaplia, Halyna Klym
2022 conf
DESSERT
Roman Diachok, Halyna Klym
2022 conf
DESSERT
Mykhailo Kvasnii, Vladyslav Shevchuk, Halyna Klym
2021 conf
IDAACS
Yevheniia Zhyvaha, Halyna Klym, Roman Dunets
2019 conf
ACIT
Volodymyr Gryga, Yaroslav Nykolaychuk, Lyubov Nyckolaychuk, Natalia Vozna, Halyna Klym
2017 conf
CSIT (1)
Sergiy Yemelyanenko, Andriy Ivanusa, Halyna Klym
2015 conf
IDAACS
Jian-Rong Li, Roman V. Kochan, Orest Kochan, Halyna Klym
2015 Misc conf
CSIT
Roman Dunets, Halyna Klym, Roman V. Kochan
2014 J jnl
Microelectron. Reliab.
Halyna Klym, V. O. Balitska, O. I. Shpotyuk, Ivan Hadzaman
2014 conf
MIXDES
Halyna Klym, Ivan Hadzaman, Iryna Yurchak
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"