Onyeka Ezenwoye

36 papers A 2B 1C 10Misc 2Journal 5Unranked 16
YearRankTypeTitle / Venue / Authors
2024 conf
ISDFS
Onyeka Ezenwoye, Eduard Pinconschi
2024 J jnl
J. Object Technol.
Onyeka Ezenwoye
2023 C conf
FIE
Yi Liu, Onyeka Ezenwoye
2022 conf
ACM Southeast Regional Conference
Onyeka Ezenwoye, Yi Liu
2022 conf
RE Workshops
Onyeka Ezenwoye, Yi Liu
2022 J jnl
CoRR
Onyeka Ezenwoye, Yi Liu
2021 C conf
SEKE
Alok Chandrakant Ratnaparkhi, Onyeka Ezenwoye, Yi Liu
2021 conf
SecDev
David Lee, Brandon Steed, Yi Liu, Onyeka Ezenwoye
2020 C conf
SEKE
Onyeka Ezenwoye, Yi Liu, William Patten
2020 conf
VR Workshops
Drew Alexander, Thuy Nguyen, Patrick Keller, Jason Orlosky, Shilpa Brown, Elena Wood, Onyeka Ezenwoye, Wanda Jirau-Rosaly
2019 conf
ACM Southeast Regional Conference
Jason Orlosky, Onyeka Ezenwoye, Heather Yates, Gina Besenyi
2019 C conf
FIE
Onyeka Ezenwoye
2018 C conf
FIE
Onyeka Ezenwoye
2015 conf
SCC
Onyeka Ezenwoye, Seyed Masoud Sadjadi, Wei Wang
2015 J jnl
Bioinform.
Sung-Hwan Kim, Onyeka Ezenwoye, Hwan-Gue Cho, Keith D. Robertson, Jeong-Hyeon Choi
2014 conf
ICCAC
Onyeka Ezenwoye, S. Masoud Sadjadi
2013 conf
IEEE SCC
Onyeka Ezenwoye, Minakshi Pokharel
2010 C conf
SEKE
Selim Kalayci, Gargi Dasgupta, Liana Fong, Onyeka Ezenwoye, Seyed Masoud Sadjadi
2010 conf
WEBIST (1)
Onyeka Ezenwoye, Salome Busi, Seyed Masoud Sadjadi
2010 J jnl
IEEE Internet Comput.
Onyeka Ezenwoye, M. Brian Blake, Gargi Dasgupta, Seyed Masoud Sadjadi, Selim Kalayci, Liana L. Fong
2010 A conf
ICWS
Onyeka Ezenwoye, Bin Tang
2009 C conf
SEKE
Onyeka Ezenwoye, Balaji Viswanathan, Seyed Masoud Sadjadi, Liana Fong, Gargi Dasgupta, Selim Kalayci
2008 C conf
SEKE
Onyeka Ezenwoye, Seyed Masoud Sadjadi
2008 J jnl
J. Networks
Onyeka Ezenwoye, Seyed Masoud Sadjadi
2008 A conf
ICSOC
Selim Kalayci, Onyeka Ezenwoye, Balaji Viswanathan, Gargi Dasgupta, Seyed Masoud Sadjadi, Liana Fong
2008 C conf
SEKE
Gargi Dasgupta, Onyeka Ezenwoye, Liana Fong, Selim Kalayci, Seyed Masoud Sadjadi, Balaji Viswanathan
2008 conf
ICAC
Gargi Dasgupta, Onyeka Ezenwoye, Liana Fong, Selim Kalayci, Seyed Masoud Sadjadi, Balaji Viswanathan
2008 conf
Mardi Gras Conference
Seyed Masoud Sadjadi, Liana Fong, Rosa M. Badia, Javier Figueroa, Javier Delgado, Xabriel J. Collazo-Mojica, Khalid Saleem, Raju Rangaswami, Shu Shimizu, Hector A. Duran-Limon, Pat Welsh, Sandeep Pattnaik, Anthony Praino, David Villegas, Selim Kalayci, Gargi Dasgupta, Onyeka Ezenwoye, Juan Carlos Martínez, Ivan Rodero, Shuyi Chen, Javier Muñoz, Diego R. López, Julita Corbalán, Hugh Willoughby, Michael McFail, Christine L. Lisetti, Malek Adjouadi
2007 conf
OTM Conferences (2)
Onyeka Ezenwoye, Seyed Masoud Sadjadi, Ariel Cary, Michael Robinson
2007 C conf
ISADS
Onyeka Ezenwoye, Seyed Masoud Sadjadi
2007 conf
WEBIST (1)
Onyeka Ezenwoye, Seyed Masoud Sadjadi
2006 conf
ACM Southeast Regional Conference
Onyeka Ezenwoye, Seyed Masoud Sadjadi
2006 conf
ICEIS (1)
Onyeka Ezenwoye, Seyed Masoud Sadjadi
2006 Misc conf
High Performance Computing Workshop
Rosa M. Badia, Gargi Dasgupta, Onyeka Ezenwoye, Liana Fong, Howard Ho, Sawsan Khuri, Yanbin Liu, Steven Luis, Anthony Praino, Jean-Pierre Prost, Ahmed Radwan, Seyed Masoud Sadjadi, Shivkumar Shivaji, Balaji Viswanathan, Pat Welsh, Akmal A. Younis
2004 Misc conf
IRI
Li Yang, Raimund K. Ege, Onyeka Ezenwoye, Qasem Kharma
2004 B conf
MUM
Onyeka Ezenwoye, Raimund K. Ege, Li Yang, Qasem Kharma
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"