Rafael de Amorim Silva

14 papers B 4Misc 1Journal 7Unranked 1
YearRankTypeTitle / Venue / Authors
2024 B conf
ICALT
Andreza Ferreira, Wilk Oliveira, Rafael de Amorim Silva, Juho Hamari, Seiji Isotani
2023 J jnl
Univers. Access Inf. Soc.
Denys F. S. Rocha, Ig Ibert Bittencourt, Rafael de Amorim Silva, Patrícia Leone Espinheira
2022 J jnl
Int. J. Uncertain. Fuzziness Knowl. Based Syst.
Bruno Almeida Pimentel, Rafael de Amorim Silva, Jadson Crislan Santos Costa
2022 J jnl
Educ. Inf. Technol.
Sivaldo Joaquim, Ig Ibert Bittencourt, Rafael de Amorim Silva, Patrícia Leone Espinheira, Marcelo Reis
2020 Misc conf
ICNC
Leandro Melo de Sales, Wendell Soares, Rafael de Amorim Silva, Thiago Bruno Melo de Sales, Karan Verma, Eduardo Setton
2020 J jnl
IEEE Internet Things J.
Rafael de Amorim Silva, Rosana T. Vaccare Braga
2020 J jnl
IEEE Syst. J.
Rafael de Amorim Silva, Rosana T. Vaccare Braga
2018 conf
ECSA (Companion)
Rafael de Amorim Silva, Rosana T. V. Braga
2017 J jnl
Int. J. Semantic Web Inf. Syst.
Armando Barbosa, Ig Ibert Bittencourt, Sean Wolfgand Matsui Siqueira, Rafael de Amorim Silva, Ivo Calado
2016 B conf
ICALT
Sivaldo J. de Santana, Ranilson Paiva, Ig Ibert Bittencourt, Patrícia Espinheira Ospina, Rafael de Amorim Silva, Seiji Isotani
2015 J jnl
Revista Brasileira de Informática na Educ.
Denys F. S. Rocha, Ig Ibert Bittencourt Santana Pinto, Rafael de Amorim Silva
2014
Rafael de Amorim Silva
2012 B conf
WCNC
Leandro Melo de Sales, Rafael de Amorim Silva, Hyggo Oliveira de Almeida, Angelo Perkusich
2009 B conf
WCNC
Rafael de Amorim Silva, Paulo André da Silva Gonçalves
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"