Vangelis Malamas

17 papers B 1C 5Journal 4Unranked 7
YearRankTypeTitle / Venue / Authors
2025 C conf
CoDIT
Dimitris Koutras, Michalis Karamousadakis, Giannis Konstantinidis, Christos Grigoriadis, Vangelis Malamas, Panayiotis Kotzanikolaou
2025 conf
IOTSMS
Dimitris Koutras, Nikolaos Fokos, Vangelis Malamas, Panayiotis Kotzanikolaou
2025 C conf
CoDIT
Panagiotis Giannopoulos, Vangelis Malamas, Thomas K. Dasaklis
2025 conf
IOTSMS
Vangelis Malamas, Dimitris Koutras, Panagiotis Giannopoulos
2025 J jnl
IEEE Internet Things J.
Vangelis Malamas, Panayiotis Kotzanikolaou, Konstantinos Nomikos, Christos Zonios, Vasileios Tenentes, Mihalis Psarakis
2025 conf
IISA
Panagiotis Giannopoulos, Vangelis Malamas, Vassilios S. Verykios, Thomas K. Dasaklis
2025 C conf
CoDIT
Thomas K. Dasaklis, Panagiotis Giannopoulos, Vangelis Malamas, Georgios Tantis, Constantinos Patsakis
2025 C conf
CoDIT
Panagiotis Giannopoulos, Vangelis Malamas, Dimitris Koutras, Thomas K. Dasaklis
2024 conf
PCI
Dimitris Koutras, Giorgos Dimitrakopoulos, Vangelis Malamas, Panayiotis Kotzanikolaou, Christos Douligeris
2024 conf
PCI
Panagiotis Giannopoulos, Vangelis Malamas, Thomas K. Dasaklis
2024 conf
PCI
Dimitrios Antoniadis, Panagiotis Giannopoulos, Vangelis Malamas, Panos T. Chountalas, Yannis A. Pollalis, Thomas K. Dasaklis
2023 J jnl
J. Theor. Appl. Electron. Commer. Res.
Thomas K. Dasaklis, Vangelis Malamas
2023 C conf
CoDIT
Vangelis Malamas, Thomas K. Dasaklis, Theodore G. Voutsinas, Panayiotis Kotzanikolaou
2023 conf
SEEDA-CECNSM
Vangelis Malamas, Dimitris Koutras, Panayiotis Kotzanikolaou
2021 J jnl
IEEE Access
Vangelis Malamas, Fotis Chantzis, Thomas K. Dasaklis, George Stergiopoulos, Panayiotis Kotzanikolaou, Christos Douligeris
2020 J jnl
IEEE Access
Vangelis Malamas, Panayiotis Kotzanikolaou, Thomas K. Dasaklis, Mike Burmester
2019 B conf
SERVICES
Vangelis Malamas, Thomas K. Dasaklis, Panayiotis Kotzanikolaou, Mike Burmester, Sokratis K. Katsikas
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"