Manjubala Bisi

18 papers Journal 8Unranked 8
YearRankTypeTitle / Venue / Authors
2025 J jnl
J. Supercomput.
Pravali Manchala, Manjubala Bisi
2025 ed.
CoCoLe
Sanjaya Kumar Panda, Rajkumar Buyya, Rashmi Ranjan Rout, Manjubala Bisi, Sangharatna Godboley, Kuan-Ching Li, Ashish Ghosh
2025 J jnl
Clust. Comput.
Sarika Mustyala, Manjubala Bisi
2024 J jnl
Autom. Softw. Eng.
Pravali Manchala, Manjubala Bisi
2024 ed.
CoCoLe
Sanjaya Kumar Panda, Rashmi Ranjan Rout, Manjubala Bisi, Ravi Chandra Sadam, Kuan-Ching Li, Vincenzo Piuri
2024 J jnl
Multim. Tools Appl.
Manjubala Bisi, Rahul Maurya
2024 J jnl
Soft Comput.
Pravali Manchala, Ankur Tiwari, Manjubala Bisi
2024 conf
ICCCNT
Manjubala Bisi, Aryan Srivastava, Abhishek Verma, Aditya Kumar
2024 conf
ICCCNT
Diksha Jain, Pravali Manchala, Sarika Mustyala, Manjubala Bisi
2024 conf
ICCCNT
Vishwank Vikram Singh, Manjubala Bisi
2024 J jnl
Clust. Comput.
Pravali Manchala, Manjubala Bisi
2023 conf
ICCCNT
Deepanshu Jindal, Yash Kumawat, Aditya Kumar Sharma, Manjubala Bisi
2023 conf
ICCCNT
Sai Vivek Kadali, Sai Vamsi Sahukari, Vasantha Eswari Devi Kalyanam, Manjubala Bisi
2022 J jnl
Appl. Soft Comput.
Pravali Manchala, Manjubala Bisi
2022 conf
ICCCNT
Pravali Manchala, Manjubala Bisi
2021 conf
ICCCNT
Kapil Ravi Rathod, Shruti Bansal, Yash Harish Chandra Kandpal, Manjubala Bisi
2021 conf
ICCCNT
Mohammad Mushtaq Ahmed, Bura Santhi Kiran, Pododdi Harshavardhan Sai, Manjubala Bisi
2016 J jnl
IET Softw.
Manjubala Bisi, Neeraj Kumar Goyal
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"