Ibrahim Farhat

17 papers A 1B 2C 3Misc 1Journal 7Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Vitor Gaboardi Dos Santos, Ibrahim Khadraoui, Ibrahim Farhat, Hamza Yous, Samy Teffahi, Hakim Hacid
2026 conf
ICPE Companion
Dominik Scheinert, Alexander Acker, Thorsten Wittkopp, Soeren Becker, Hamza Yous, Karnakar Reddy, Ibrahim Farhat, Hakim Hacid, Odej Kao
2026 J jnl
CoRR
Dominik Scheinert, Alexander Acker, Thorsten Wittkopp, Sören Becker, Hamza Yous, Karnakar Reddy, Ibrahim Farhat, Hakim Hacid, Odej Kao
2025 J jnl
Signal Process. Image Commun.
Ahmed Telili, Wassim Hamidouche, Ibrahim Farhat, Hadi Amirpour, Christian Timmerer, Ibrahim Khadraoui, Jiajie Lu, The Van Le, Jeonneung Baek, Jin Young Lee, Yiying Wei, Xiaopeng Sun, Yu Gao, Jiancheng Huang, Yujie Zhong
2025 conf
FLLM
Justus Flerlage, Alexander Acker, Dominik Scheinert, Soeren Becker, Hamza Yous, Karnakar Reddy, Ibrahim Farhat, Hakim Hacid, Odej Kao
2024 conf
GMSys@MMSys
Ibrahim Farhat, Ibrahim Khadraoui, Wassim Hamidouche, Mohit K. Sharma
2024 B conf
WCNC
Mohit K. Sharma, Ibrahim Farhat, Wassim Hamidouche
2024 B conf
ICIP
Ahmed Telili, Ibrahim Farhat, Wassim Hamidouche, Hadi Amirpour
2024 J jnl
IEEE Open J. Commun. Soc.
Mohit K. Sharma, Ibrahim Farhat, Chen-Feng Liu, Nassim Sehad, Wassim Hamidouche, Mérouane Debbah
2023 C conf
MMSP
Ibrahim Farhat, Pierre-Loup Cabarat, Daniel Ménard, Wassim Hamidouche, Olivier Déforges
2023 C conf
ISCAS
Ibrahim Farhat, Pierre-Loup Cabarat, Wassim Hamidouche, Patrice Angot, Philipe Gonon, Daniel Ménard
2023 A conf
MMSys
Marko Viitanen, Joose Sainio, Alexandre Mercat, Guillaume Gautier, Jarno Vanne, Ibrahim Farhat, Pierre-Loup Cabarat, Wassim Hamidouche, Daniel Ménard
2023 J jnl
J. Real Time Image Process.
Anup Saha, Wassim Hamidouche, Miguel Chavarrías, Fernando Pescador, Ibrahim Farhat
2022 J jnl
IEEE Trans. Consumer Electron.
Ibrahim Farhat, Wassim Hamidouche, Adrien Grill, Daniel Ménard, Olivier Déforges
2022 C conf
PCS
Ibrahim Farhat, Wassim Hamidouche, Adrien Grill, Daniel Ménard, Olivier Déforges
2021 J jnl
IEEE Trans. Consumer Electron.
Ibrahim Farhat, Wassim Hamidouche, Adrien Grill, Daniel Ménard, Olivier Déforges
2020 Misc conf
ICASSP
Ibrahim Farhat, Wassim Hamidouche, Adrien Grill, Daniel Ménard, Olivier Déforges
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"