Nathaniel R. Morgan

27 papers Journal 27
YearRankTypeTitle / Venue / Authors
2025 J jnl
CoRR
Joshua Vedral, Nathaniel R. Morgan, Dmitri Kuzmin, Jacob Moore
2024 J jnl
Comput. Phys. Commun.
Caleb O. Yenusah, Nathaniel R. Morgan, Ricardo A. Lebensohn, Miroslav Zecevic, Marko Knezevic
2024 J jnl
Inf.
Nathaniel R. Morgan, Caleb O. Yenusah, Adrian Diaz, Daniel Dunning, Jacob L. Moore, Erin Heilman, Evan J. Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Russell Marki, Robert Robey, Marko Knezevic
2024 J jnl
Inf.
Nathaniel R. Morgan, Caleb O. Yenusah, Adrian Diaz, Daniel Dunning, Jacob L. Moore, Erin Heilman, Calvin Roth, Evan J. Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey
2022 J jnl
J. Comput. Appl. Math.
Xiaodong Liu, Nathaniel R. Morgan, Evan J. Lieberman, Donald E. Burton
2021 J jnl
CoRR
Xiaodong Liu, Nathaniel R. Morgan, Evan J. Lieberman, Donald E. Burton
2021 J jnl
J. Parallel Distributed Comput.
Daniel Dunning, Nathaniel R. Morgan, Jacob L. Moore, Eappen Nelluvelil, Tanya V. Tafolla, Robert W. Robey
2020 J jnl
J. Comput. Phys.
Konstantin Lipnikov, Nathaniel R. Morgan
2020 J jnl
SIAM J. Sci. Comput.
Rémi Abgrall, Konstantin Lipnikov, Nathaniel R. Morgan, Svetlana V. Tokareva
2019 J jnl
Comput. Math. Appl.
Vincent P. Chiravalle, Nathaniel R. Morgan
2019 J jnl
J. Comput. Phys.
Xiaodong Liu, Nathaniel R. Morgan, Donald E. Burton
2019 J jnl
J. Comput. Phys.
Konstantin Lipnikov, Nathaniel R. Morgan
2019 J jnl
J. Comput. Phys.
Konstantin Lipnikov, Nathaniel R. Morgan
2019 J jnl
Comput. Math. Appl.
Evan J. Lieberman, Nathaniel R. Morgan, Darby J. Luscher, Donald E. Burton
2019 J jnl
Comput. Math. Appl.
Tong Wu, Mikhail J. Shashkov, Nathaniel R. Morgan, Dmitri Kuzmin, H. Luo
2019 J jnl
Comput. Math. Appl.
Andrew Barlow, Nathaniel R. Morgan, Mikhail J. Shashkov
2019 J jnl
SoftwareX
Jacob L. Moore, Nathaniel R. Morgan, Mark F. Horstemeyer
2018 J jnl
J. Comput. Phys.
Donald E. Burton, Nathaniel R. Morgan, Marc R. Charest, Mark A. Kenamond, J. Fung
2018 J jnl
J. Comput. Phys.
Xiaodong Liu, Nathaniel R. Morgan, Donald E. Burton
2018 J jnl
J. Comput. Phys.
Nathaniel R. Morgan, Xiaodong Liu, Donald E. Burton
2017 J jnl
J. Comput. Phys.
Nathaniel R. Morgan, Jacob I. Waltz
2015 J jnl
J. Comput. Phys.
Nathaniel R. Morgan, Jacob I. Waltz, Donald E. Burton, Marc R. Charest, Thomas R. Canfield, John G. Wohlbier
2015 J jnl
J. Comput. Phys.
Nathaniel R. Morgan, Jacob I. Waltz, Donald E. Burton, Marc R. Charest, Thomas R. Canfield, John G. Wohlbier
2015 J jnl
J. Comput. Phys.
Donald E. Burton, Nathaniel R. Morgan, Theodore C. Carney, Mark A. Kenamond
2014 J jnl
J. Comput. Phys.
Nathaniel R. Morgan, Konstantin Lipnikov, Donald E. Burton, Mark A. Kenamond
2014 J jnl
J. Comput. Phys.
Jacob I. Waltz, Thomas R. Canfield, Nathaniel R. Morgan, L. D. Risinger, John G. Wohlbier
2013 J jnl
J. Comput. Phys.
Nathaniel R. Morgan, Mark A. Kenamond, Donald E. Burton, Theodore C. Carney, Daniel Ingraham
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"