Vazhora Malayil Manikandan

27 papers Journal 10Unranked 17
YearRankTypeTitle / Venue / Authors
2025 J jnl
CAAI Trans. Intell. Technol.
Shaiju Panchikkil, Vazhora Malayil Manikandan, Partha Pratim Roy, Shuihua Wang, Yu-Dong Zhang
2025 J jnl
AI Soc.
Anitha Rani Inturi, Vazhora Malayil Manikandan, Yu-Chen Hu
2024 J jnl
Multim. Tools Appl.
Shaiju Panchikkil, Vazhora Malayil Manikandan
2024 conf
ICCCNT
Manohar Makkena, Geyani Lingamallu, Veda Harshitha Digavalli, Vamshidhar Reddy Gudupalli, Vazhora Malayil Manikandan, Shaiju Panchikkil
2024 conf
ICCCNT
Devisetty Sai Tharun, Panguluri Sai Srija, Peeta Vamsi Krishna, Shaiju Panchikkil, Vazhora Malayil Manikandan
2024 conf
ICPR (16)
Anitha Rani Inturi, Vazhora Malayil Manikandan, Partha Pratim Roy, Byung-Gyu Kim
2023 J jnl
Entropy
Shaiju Panchikkil, Vazhora Malayil Manikandan, Yudong Zhang, Shuihua Wang
2023 conf
NCC
Shaiju Panchikkil, Vaibav Reddy Malpeddi, Vazhora Malayil Manikandan
2023 conf
ICCCNT
Sai Naveen Katla, K. Nikhila Korivi, Vazhora Malayil Manikandan
2023 J jnl
Sensors
Anitha Rani Inturi, Vazhora Malayil Manikandan, Mahamkali Naveen Kumar, Shuihua Wang, Yudong Zhang
2022 J jnl
Int. J. Comput. Sci. Eng.
Tankala Yuvaraj, Joseph K. Paul, Vazhora Malayil Manikandan
2022 J jnl
Multim. Tools Appl.
Shaiju Panchikkil, Vazhora Malayil Manikandan, Yu-Dong Zhang
2022 J jnl
Signal Process. Image Commun.
Vazhora Malayil Manikandan, Yu-Dong Zhang
2022 conf
ICCCNT
Medha Jha, Ananya Tiwari, Mudigonda Himansh, Vazhora Malayil Manikandan
2021 conf
ICCCNT
K. Nikhila Korivi, Vazhora Malayil Manikandan
2021 conf
ICCCNT
Yelisetty Srivarsha, Vazhora Malayil Manikandan
2021 conf
ICCCNT
Shaiju Panchikkil, Vazhora Malayil Manikandan
2021 conf
ICMC
Vazhora Malayil Manikandan
2021 J jnl
Comput. Sci.
Vazhora Malayil Manikandan
2021 conf
ICCCNT
K. Jagruth, Vazhora Malayil Manikandan, Ravi Kant Kumar
2020 conf
ICCCNT
Kandala Sree Rama Murthy, Vazhora Malayil Manikandan
2020 conf
ICIIS
Ravi Srihitha, Yadlapalli Sai Harshini, Vazhora Malayil Manikandan
2020 conf
ICCCS
K. Nikhila Korivi, Vazhora Malayil Manikandan
2019 conf
NextComp
Vazhora Malayil Manikandan, Vedhanayagam Masilamani
2018 J jnl
Comput. Electr. Eng.
Vazhora Malayil Manikandan, V. Masilamani
2016 conf
ICVGIP
Vazhora Malayil Manikandan, V. Masilamani
2014 conf
FICTA (1)
Shaik K. Ayesha, Vazhora Malayil Manikandan, V. Masilamani
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"