Kamil Kuliberda

12 papers A 1Journal 3Unranked 8
YearRankTypeTitle / Venue / Authors
2009 conf
OTM Workshops
Tomasz Marek Kowalski, Kamil Kuliberda, Jacek Wislicki, Radoslaw Adamus
2009 conf
ICEIS (1)
Tomasz Marek Kowalski, Kamil Kuliberda, Jacek Wislicki, Radoslaw Adamus
2009 J jnl
Ann. UMCS Informatica
Tomasz Marek Kowalski, Michal Chromiak, Kamil Kuliberda, Jacek Wislicki, Radoslaw Adamus, Kazimierz Subieta
2008 conf
ICOODB
Jacek Wislicki, Kamil Kuliberda, Tomasz Marek Kowalski, Radoslaw Adamus, Kazimierz Subieta
2008 conf
ICOODB
Tomasz Marek Kowalski, Jacek Wislicki, Kamil Kuliberda, Radoslaw Adamus, Kazimierz Subieta
2007 J jnl
Multiagent Grid Syst.
Kamil Kuliberda, Radoslaw Adamus, Jacek Wislicki, Krzysztof Kaczmarski, Tomasz Marek Kowalski, Kazimierz Subieta
2007 J jnl
Int. J. Bus. Process. Integr. Manag.
Jacek Wislicki, Kamil Kuliberda, Radoslaw Adamus, Kazimierz Subieta
2006 conf
OTM Conferences (2)
Kamil Kuliberda, Radoslaw Adamus, Jacek Wislicki, Krzysztof Kaczmarski, Tomasz Marek Kowalski, Kazimierz Subieta
2006 conf
BIS
Kamil Kuliberda, Jacek Wislicki, Radoslaw Adamus, Kazimierz Subieta
2006 A conf
ICSOC
Kamil Kuliberda, Jacek Wislicki, Tomasz Marek Kowalski, Radoslaw Adamus, Krzysztof Kaczmarski, Kazimierz Subieta
2006 conf
DEXA Workshops
Kamil Kuliberda, Piotr Blaszczyk, Grzegorz Balcerzak, Krzysztof Kaczmarski, Radoslaw Adamus, Kazimierz Subieta
2005 conf
OTM Workshops
Kamil Kuliberda, Jacek Wislicki, Radoslaw Adamus, Kazimierz Subieta
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"