Raj Naidoo

16 papers B 1C 3Journal 7Unranked 5
YearRankTypeTitle / Venue / Authors
2025 B conf
IJCNN
Halleluyah Kupolati, Ganesh K. Venayagamoorthy, Njoroge Gitau, Raj Naidoo
2025 conf
ICECET
Rajitha Wattegama, Michael Short, Geetika Aggarwal, Raj Naidoo
2024 J jnl
IEEE Trans. Ind. Electron.
Jingyuan Wu, Shiming Hu, Abhishek Kumar, Raj Naidoo, Yan Deng
2023 C conf
IECON
Shiming Hu, Hao Qin, Yi Wang, Yan Deng, Huan Yang, Raj Naidoo
2023 conf
AFRICON
Mandisi Gwabavu, Ramesh C. Bansal, Raj Naidoo, Siphokazi Pemba
2022 J jnl
IEEE Syst. J.
Manohar Mishra, Bhaskar Patnaik, Ramesh C. Bansal, Raj Naidoo, Bignaraj Naik, Janmenjoy Nayak
2021 J jnl
IEEE Syst. J.
Ndaedzo M. Moyo, Ramesh C. Bansal, Raj Naidoo
2021 C conf
ICINCO
Nsilulu T. Mbungu, Raj Naidoo, Ramesh C. Bansal, Mukwanga W. Siti
2020 C conf
IECON
G. R. Krüger, Raj Naidoo, Ramesh C. Bansal, Nsilulu T. Mbungu
2020 J jnl
IEEE Access
Thabo G. Hlalele, Raj Naidoo, Jiangfeng Zhang, Ramesh C. Bansal
2019 conf
ISC2
Raj Naidoo, Marnó Zietsman
2019 J jnl
IEEE Access
Ndaedzo M. Moyo, Ramesh C. Bansal, Raj Naidoo, Willem Sprong
2019 J jnl
IEEE Access
Nsilulu T. Mbungu, Raj Naidoo, Ramesh C. Bansal, Vahid Vahidinasab
2018 J jnl
IEEE Access
Ranjay Singh, Ramesh C. Bansal, Arvind R. Singh, Raj Naidoo
2017 conf
ISGT Europe
Ewald Erasmus, Raj Naidoo
2011 conf
AFRICON
Vusumuzi Dlamini, Raj Naidoo, Marubini Manyage
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"