Rajib Kumar Rana

24 papers B 3C 1Misc 4Journal 11Unranked 4
YearRankTypeTitle / Venue / Authors
2017 conf
IPSN
Wanli Xue, Chengwen Luo, Guohao Lan, Rajib Kumar Rana, Wen Hu, Aruna Seneviratne
2016 C conf
ICOST
Ying Su, Rajib Kumar Rana, Frank Whittaker, Jeffrey Soar
2016 Misc conf
SenSys
Wanli Xue, Chengwen Luo, Rajib Kumar Rana, Wen Hu, Aruna Seneviratne
2016 J jnl
IEEE Pervasive Comput.
Rajib Kumar Rana, Margee Hume, John Reilly, Raja Jurdak, Jeffrey Soar
2016 J jnl
EAI Endorsed Trans. Scalable Inf. Syst.
Rajib Kumar Rana, Margee Hume, John Reilly, Sabesan Sivapalan, Jeffrey Soar
2015 J jnl
Pervasive Mob. Comput.
Rajib Kumar Rana, Chun Tung Chou, Nirupama Bulusu, Salil S. Kanhere, Wen Hu
2015 J jnl
CoRR
Rajib Kumar Rana, Margee Hume, John Reilly, Raja Jurdak, Jeffrey Soar
2015 J jnl
CoRR
Rajib Kumar Rana, Margee Hume, John Reilly, Jeffrey Soar
2014 J jnl
CoRR
Mohamad Trad, Raja Jurdak, Rajib Kumar Rana
2014 J jnl
CoRR
Rajib Kumar Rana, Brano Kusy, Josh Wall, Wen Hu
2014 J jnl
CoRR
Rajib Kumar Rana, Mingrui Yang, Tim Wark, Chun Tung Chou, Wen Hu
2013 J jnl
CoRR
Rajib Kumar Rana, Mingrui Yang, Tim Wark, Chun Tung Chou, Wen Hu
2013 J jnl
CoRR
Rajib Kumar Rana, Chun Tung Chou, Nirupama Bulusu, Salil S. Kanhere, Wen Hu
2013 conf
REALWSN
Branislav Kusy, Rajib Kumar Rana, Philip Valencia, Raja Jurdak, Josh Wall
2013 Misc conf
SenSys
Bo Wei, Mingrui Yang, Yiran Shen, Rajib Kumar Rana, Chun Tung Chou, Wen Hu
2013 J jnl
CoRR
Rajib Kumar Rana, Wen Hu, Chun Tung Chou
2012 conf
IPSN
Bo Wei, Mingrui Yang, Rajib Kumar Rana, Chun Tung Chou, Wen Hu
2011
Rajib Kumar Rana
2011 B conf
EWSN
Rajib Kumar Rana, Wen Hu, Tim Wark, Chun Tung Chou
2011 Misc conf
DICTA
Sabesan Sivapalan, Rajib Kumar Rana, Daniel Chen, Sridha Sridharan, Simon Denman, Clinton Fookes
2010 conf
IPSN
Rajib Kumar Rana, Chun Tung Chou, Salil S. Kanhere, Nirupama Bulusu, Wen Hu
2010 B conf
EWSN
Rajib Kumar Rana, Wen Hu, Chun Tung Chou
2009 Misc conf
SenSys
Rajib Kumar Rana, Chun Tung Chou, Salil S. Kanhere, Nirupama Bulusu, Wen Hu
2009 B conf
LCN
Chun Tung Chou, Rajib Kumar Rana, Wen Hu
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"