Valeriy Naumov

19 papers A 1C 1Journal 3Unranked 14
YearRankTypeTitle / Venue / Authors
2019 conf
DCCN
Valeriy Naumov, Vitalii Beschastnyi, Darya Y. Ostrikova, Yuliya Gaidamaka
2017 conf
NEW2AN
Vitalii Beschastnyi, Valeriy Naumov, Pasquale Scopelliti, Irina A. Gudkova, Claudia Campolo, Giuseppe Araniti, Iliya Dzantiev, Konstantin E. Samouylov
2017 J jnl
IEEE J. Sel. Areas Commun.
Vitaly Petrov, Dmitrii Solomitckii, Andrey K. Samuylov, Maria A. Lema, Margarita Gapeyenko, Dmitri Moltchanov, Sergey Andreev, Valeriy Naumov, Konstantin E. Samouylov, Mischa Dohler, Yevgeni Koucheryavy
2015 C conf
ISDA
Raija Halonen, Olli Martikainen, Valeriy Naumov, Ye Zhang
2015 conf
ICUMT
Valeriy Naumov, Konstantin E. Samouylov, Natalia Yarkina, Eduard S. Sopin, Sergey Andreev, Andrey K. Samuylov
2014 conf
AMCIS
Raija Halonen, Olli Martikainen, Kaisu Juntunen, Valeriy Naumov
2014 conf
ICUMT
Valeriy Naumov, Konstantin E. Samouylov, Eduard S. Sopin, Sergey D. Andreev
2013 J jnl
Autom. Control. Comput. Sci.
Valeriy Naumov, Olli Martikainen
2011 conf
ISABEL
Petri Pulli, Olli Martikainen, Ye Zhang, Valeriy Naumov, Zeeshan Asghar, Antti Pitkänen
2011 conf
GPC Workshops
Ye Zhang, Olli Martikainen, Petri Pulli, Valeriy Naumov
2011 conf
ISABEL
Ye Zhang, Olli Martikainen, Petri Pulli, Valeriy Naumov
2010 conf
EGES/GISP
Olli Martikainen, Raija Halonen, Valeriy Naumov
2005 conf
ICPP Workshops
Valeriy Naumov, Olli Martikainen
2003 A conf
MSWiM
Valeriy Naumov, Thomas R. Gross
2002 conf
Net-Con
Pertti Raatikainen, Olli Martikainen, Valeriy Naumov
1999 conf
MMB
Udo R. Krieger, Valeriy Naumov
1997 conf
MMB (Kurzbeiträge)
Udo R. Krieger, Valeriy Naumov, Dietmar Wagner
1995 J jnl
Telecommun. Syst.
Valeriy Naumov
1994 conf
SMARTNET
Olli Martikainen, Tapani Karttunen, Valeriy Naumov, Konstantin E. Samouylov
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"