Hani Alzaid

19 papers B 1C 1Misc 1Journal 5Unranked 11
YearRankTypeTitle / Venue / Authors
2018 J jnl
Comput. Networks
Simon Yusuf Enoch, Mengmeng Ge, Jin B. Hong, Hani Alzaid, Dong Seong Kim
2018 conf
ACSW
Simon Yusuf Enoch, Jin B. Hong, Mengmeng Ge, Hani Alzaid, Dong Seong Kim
2017 conf
TrustCom/BigDataSE/ICESS
Simon Yusuf Enoch, Mengmeng Ge, Jin B. Hong, Hani Alzaid, Dong Seong Kim
2017 conf
TrustCom/BigDataSE/ICESS
Mengmeng Ge, Jin B. Hong, Hani Alzaid, Dong Seong Kim
2013 conf
IISA
Soufiene Ben Othman, Hani Alzaid, Abdelbasset Trad, Habib Youssef
2013 conf
IFIPTM
Hani Alzaid, Manal Alfaraj, Sebastian Ries, Audun Jøsang, Muneera Albabtain, Alhanof Abuhaimed
2013 conf
TrustCom/ISPA/IUCC
Soufiene Ben Othman, Abdelbasset Trad, Habib Youssef, Hani Alzaid
2013 conf
Med-Hoc-Net
Soufiene Ben Othman, Abdelbasset Trad, Habib Youssef, Hani Alzaid
2012 J jnl
Int. J. Commun. Networks Distributed Syst.
Hani Alzaid, Ernest Foo, Juan Manuel González Nieto, DongGook Park
2012 J jnl
Ad Hoc Networks
Omar Cheikhrouhou, Anis Koubaa, Gianluca Dini, Hani Alzaid, Mohamed Abid
2012 J jnl
Secur. Commun. Networks
Hani Alzaid, Ernest Foo, Juan Manuel González Nieto, Ejaz Ahmed
2012 conf
MobiHealth
Soufiene Ben Othman, Abdelbasset Trad, Hani Alzaid, Habib Youssef
2011 conf
ANT/MobiWIS
Omar Cheikhrouhou, Anis Koubaa, Gianluca Dini, Hani Alzaid, Mohamed Abid
2010 B conf
AINA
Hani Alzaid, DongGook Park, Juan Manuel González Nieto, Ernest Foo
2009 conf
S-CUBE
Hani Alzaid, DongGook Park, Juan Manuel González Nieto, Colin Boyd, Ernest Foo
2009 J jnl
J. Digit. Content Technol. its Appl.
Hani Alzaid, Suhail Abanmi
2008 conf
NTMS
Hani Alzaid, Manal Alfaraj
2008 C conf
PDCAT
Hani Alzaid, Ernest Foo, Juan Manuel González Nieto
2008 Misc conf
AISC
Hani Alzaid, Ernest Foo, Juan Manuel González Nieto
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"