Ian Walden

27 papers Journal 17Unranked 2
YearRankTypeTitle / Venue / Authors
2022 J jnl
CoRR
Ian Walden, Johan David Michels
2019 conf
IEEE Symposium on Security and Privacy Workshops
Alexander Vetterl, Richard Clayton, Ian Walden
2018 J jnl
Comput. Law Secur. Rev.
Ian Walden
2016 J jnl
Eur. J. Law Technol.
Guido Noto La Diega, Ian Walden
2016 J jnl
Comput. Law Secur. Rev.
Niamh Christina Gleeson, Ian Walden
2016 J jnl
Int. J. Law Inf. Technol.
W. Kuan Hon, Christopher Millard, Jatinder Singh, Ian Walden, Jon Crowcroft
2014 J jnl
Eur. J. Law Technol.
Niamh Christina Gleeson, Ian Walden
2013 ch.
Cloud Computing Law
Ian Walden, Laise Da Correggio Luciano
2013 ch.
Cloud Computing Law
Ian Walden
2013 ch.
Cloud Computing Law
W. Kuan Hon, Christopher Millard, Ian Walden
2013 ch.
Cloud Computing Law
W. Kuan Hon, Christopher Millard, Ian Walden
2013 ch.
Cloud Computing Law
Simon Bradshaw, Christopher Millard, Ian Walden
2013 ch.
Cloud Computing Law
W. Kuan Hon, Christopher Millard, Ian Walden
2013 ch.
Cloud Computing Law
W. Kuan Hon, Christopher Millard, Ian Walden
2013 ch.
Cloud Computing Law
W. Kuan Hon, Christopher Millard, Ian Walden
2011 J jnl
Int. J. Law Inf. Technol.
Simon Bradshaw, Christopher Millard, Ian Walden
2010 J jnl
Comput. Law Secur. Rev.
Ian Walden
2006 J jnl
Inf. Secur. Tech. Rep.
Ian Walden
2004 conf
iTrust
Ian Walden
2002 J jnl
Int. J. Law Inf. Technol.
Ian Walden
1994 J jnl
Int. J. Law Inf. Technol.
Chris Reed, Ian Walden
1990 J jnl
Comput. Law Secur. Rev.
Stewart Dresner, Ian Walden
1989 J jnl
Comput. Law Secur. Rev.
Ian Walden
1989 J jnl
Comput. Law Secur. Rev.
Ian Walden
1988 J jnl
Comput. Law Secur. Rev.
Ian Walden
1988 J jnl
Comput. Law Secur. Rev.
Ian Walden
1988 J jnl
Comput. Law Secur. Rev.
Ian Walden
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"