Ram Prakash Sharma

28 papers Journal 18Unranked 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
IEEE Trans. Biom. Behav. Identity Sci.
Ashutosh Sharma, Tanuj, Ram Prakash Sharma
2025 J jnl
Eng. Appl. Artif. Intell.
Ram Prakash Sharma, Bimal Kumar Barik, V. Vinay Kumar, Abhishek Sharma
2025 J jnl
CoRR
Balram Singh, Ram Prakash Sharma, Somnath Dey
2025 conf
PReMI (2)
Nitansh, Ashutosh Sharma, Ram Prakash Sharma
2025 J jnl
Neural Comput. Appl.
Pradyumna Kumar Pattnaik, S. R. Mishra, Abhishek Sharma, Ram Prakash Sharma
2025 J jnl
Eng. Appl. Artif. Intell.
Abhishek Sharma, Ram Prakash Sharma
2024 conf
CVMI
Tanuj, Ram Prakash Sharma
2024 J jnl
Multim. Tools Appl.
Anuj Rai, Ashutosh Anshul, Ashwini Jha, Prayag Jain, Ram Prakash Sharma, Somnath Dey
2023 J jnl
SN Comput. Sci.
Ashutosh Anshul, Ashwini Jha, Prayag Jain, Anuj Rai, Ram Prakash Sharma, Somnath Dey
2023 J jnl
CoRR
Anuj Rai, Ashutosh Anshul, Ashwini Jha, Prayag Jain, Ram Prakash Sharma, Somnath Dey
2023 J jnl
CoRR
Anuj Rai, Parsheel Kumar Tiwari, Jyotishna Baishya, Ram Prakash Sharma, Somnath Dey
2023 conf
CVIP (2)
Shaik Dastagiri, Kongara Sireesh, Ram Prakash Sharma
2021 J jnl
Multim. Tools Appl.
Ram Prakash Sharma, Somnath Dey
2021 conf
PReMI
Ashutosh Anshul, Ashwini Jha, Prayag Jain, Anuj Rai, Ram Prakash Sharma, Somnath Dey
2021 J jnl
Math. Comput. Simul.
Mohamed M. Khader, Ram Prakash Sharma
2019 J jnl
J. Electronic Imaging
Ram Prakash Sharma, Somnath Dey
2019 J jnl
Vis. Comput.
Ram Prakash Sharma, Somnath Dey
2019 conf
MIKE
Ram Prakash Sharma, Ashutosh Anshul, Ashwini Jha, Somnath Dey
2019 conf
PReMI (1)
Ram Prakash Sharma, Somnath Dey
2019 conf
CAIP (1)
Ram Prakash Sharma, Somnath Dey
2019 conf
MIKE
Ram Prakash Sharma, Somnath Dey
2019 J jnl
Image Vis. Comput.
Ram Prakash Sharma, Somnath Dey
2018 J jnl
CoRR
Ram Prakash Sharma, Somnath Dey
2018 J jnl
CoRR
Ram Prakash Sharma, Somnath Dey
2017 conf
MIKE
Ram Prakash Sharma, Somnath Dey
2016 J jnl
J. Comput. Des. Eng.
Kalidas Das, Ram Prakash Sharma, Amit Sarkar
2015 conf
ICACCI
Ram Prakash Sharma, Rajarshi Pal
2013 J jnl
Int. J. Speech Technol.
Ram Prakash Sharma, Omar Farooq, Israr Khan
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"