Ralph M. Kennel

16 papers C 1Journal 12Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
IEEE Access
Mohammed Azharuddin Shamshuddin, Ralph M. Kennel, Diego Verdugo
2022 J jnl
IEEE Access
Matthias Laumann, Christian Weiner, Ralph M. Kennel
2018 J jnl
IEEE Trans. Control. Syst. Technol.
Florian Bauer, Christoph M. Hackl, Keyue Ma Smedley, Ralph M. Kennel
2018 J jnl
IEEE Trans. Ind. Informatics
S. Alireza Davari, Fengxiang Wang, Ralph M. Kennel
2017 conf
ISIE
Anton H. Tamas, Claudia S. Martis, Simon Wiedemann, Ralph M. Kennel
2017 J jnl
IEEE Trans. Ind. Electron.
Yuanlin Wang, Xiaocan Wang, Wei Xie, Fengxiang Wang, Manfeng Dou, Ralph M. Kennel, Robert D. Lorenz, Dieter Gerling
2017 conf
ISIE
Simon Wiedemann, Ralph M. Kennel
2016 C conf
ACC
Florian Bauer, Christoph M. Hackl, Keyue Smedley, Ralph M. Kennel
2016 J jnl
IEEE Trans. Ind. Electron.
Esteban J. Fuentes, César A. Silva, Ralph M. Kennel
2016 J jnl
IEEE Trans. Ind. Electron.
Esteban J. Fuentes, Dante Kalise, Ralph M. Kennel
2015 J jnl
IEEE Trans. Ind. Electron.
Wei Xie, Xiaocan Wang, Fengxiang Wang, Wei Xu, Ralph M. Kennel, Dieter Gerling, Robert D. Lorenz
2015 J jnl
IEEE Trans. Ind. Informatics
Fengxiang Wang, Shihua Li, Xuezhu Mei, Wei Xie, José Rodríguez, Ralph M. Kennel
2014 J jnl
IEEE Trans. Ind. Electron.
Esteban J. Fuentes, Dante Kalise, José R. Rodríguez, Ralph M. Kennel
2014 J jnl
IEEE Trans. Ind. Electron.
Özlem Karaca, Franz Kappeler, Daniela Waldau, Ralph M. Kennel, Juergen Rackles
2014 conf
AIM
Alexander Dötlinger, Florian Larcher, Ralph M. Kennel
2008 J jnl
IEEE Trans. Ind. Electron.
Damian Giaouris, John W. Finch, Oscar C. Ferreira, Ralph M. Kennel, George Mekhael El-Murr
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"