Nazih Yacer Rebouh

11 papers Journal 11
YearRankTypeTitle / Venue / Authors
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Jian Shen, Zeeshan Zafar, Shah Fahd, Nazih Yacer Rebouh, Habib Kraiem, Reimund P. Rötter, Muhammad Habib ur Rahman
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Huyong Yan, Asad Khan, Ahsan Jamil, Belkendil Abdeldjalil, Taoufik Saidani, Nazih Yacer Rebouh
2025 J jnl
IEEE Access
Mohamed Wahba, Emad Mabrouk, Youssef M. Youssef, Mohamed Saber, Nassir S. Al-Arifi, Nazih Yacer Rebouh, Mahmoud M. Mansour
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Qin Wang, Linlin Lu, Qingting Li, Muhammad Mubbin, Shaker Ul Din, Hela Elmannai, Yahia F. Said, Nazih Yacer Rebouh
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Ning Tang, Muhammad Farhan, Pir Mohammad, Mohammad Abdullah-Al-Wadud, Saddam Hussain, Umair Hamza, Rana Muhammad Zulqarnain, Nazih Yacer Rebouh
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Xiaofei Han, Nazih Yacer Rebouh, Yasmeen Ahmed, Muhammad Nasar Ahmad, Zainab Tahir, Yahia F. Said, Ishfaq Gujree
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Bin Zhu, Ahsen Maqsoom, Lapyote Prasittisopin, Chaudhary Danyal Aslam, Umer Khalil, Sahar Zia, Niamat Ullah, Mohammad Abdullah-Al-Wadud, Nazih Yacer Rebouh
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Shumin Han, Runhong Gao, Liaqat Ali Waseem, Kasye Shitu, Nazih Yacer Rebouh, Rana Muhammad Zulqarnain, Yahia F. Said
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Pu Bu, Rana Waqar Aslam, Abdul Quddoos, Nazih Yacer Rebouh, Muhammad Nasar Ahmad, Rana Muhammad Zulqarnain, Qaiser Abbas, Yahia F. Said
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Xiangtian Zheng, Siqi Zhang, Wei Huang, Muhammad Burhan Khalid, Tibra Ishaq, Habib Kraiem, Nazih Yacer Rebouh, Dmitry Kucher
2025 J jnl
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Jinsong Zhang, Bochi Zou, Yifei Yuan, Asad Khan, Muhammad Bilawal Junaid, Qaiser Abbas, Rana Muhammad Zulqarnain, Nazih Yacer Rebouh, Olga D. Kucher, Hassan Alzahrani
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"