SEVulDet

SEVulDet SEVulDet est un framework basé sur l'apprentissage profond amélioré par la sémantique qui permet de localiser avec précision les motifs de vulnérabilité en extrayant, préservant et apprenant davantage de sémantique. E-mail pour communication : [email protected] Détails de SEVulDet C...

deep-pwning

Deep-pwning est un framework léger pour expérimenter avec des modèles d'apprentissage automatique dans le but d'évaluer leur robustesse face à un adversaire motivé. Notez que deep-pwning dans son état actuel est loin d'être mature ou complet. Il est conçu pour être expérimenté, enrichi et étendu...

DeepTraffic

Modèles d'apprentissage profond pour la classification du trafic réseau Pour plus d'informations, veuillez lire nos articles. 🎓Page Google Scholar de Wei Wang Wei Wang, Xuewen Zeng, Xiaozhou Ye, Yiqiang Sheng et Ming Zhu, « Classification du trafic malveillant à l'aide de réseaux de neurones...

deep-loglizer

Deep-loglizer Deep-loglizer — это набор инструментов для анализа журналов на основе глубокого обучения, предназначенный для автоматического обнаружения аномалий. Если вы используете deep-loglizer в своих исследованиях для публикации, пожалуйста, укажите следующую статью: Zhuangbin Chen, Jinyang...

PassGAN

PassGAN Ce dépôt contient le code de l'article PassGAN: A Deep Learning Approach for Password Guessing. Le modèle de PassGAN est tiré de Improved Training of Wasserstein GANs et il est supposé que les auteurs de PassGAN ont utilisé l'implémentation TensorFlow improvedwgantraining dans leurs...

Jammer-Loc

Jammer-Loc Ce dépôt contient le code et le lien vers le jeu de données associés à l'article « Apprentissage automatique et profond pour la localisation de brouilleurs UWB en intérieur. » Exemple d'utilisation Lancez le processus d'optimisation des hyperparamètres : python hpo.py \ --task...

degas

Degas Обнаружение DGA-генерированных доменов с использованием моделей глубокого обучения Запуск В настоящее время я использую Conda Anaconda/Miniconda для разработки, но вы также можете использовать Pipenv или virtualenv с прилагаемым файлом requirements.txt. conda: conda env create -f...

awesome-latency-attacks

Awesome Deep Learning Latency Attacks & Defenses Этот репозиторий поддерживается как сопутствующий ресурс к обзору «Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey». Он индексирует статьи, ссылки на код, заметки по таксономии и иллюстрации, посвящённые угрозам доступности,...

PrivacyRaven

Примечание: этот проект находится на паузе. PrivacyRaven — это библиотека для тестирования конфиденциальности систем глубокого обучения. С её помощью можно определять восприимчивость модели к различным атакам на конфиденциальность; оценивать методы машинного обучения, сохраняющие...

TorchServe Server-Side Request Forgery vulnerability

ImpactRemote Server-Side Request Forgery SSRF Issue: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and...

On the Study of Biometric Spoofing Detection Using Deep Learning

Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models,...

The Chronicles of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting RFF has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical...

Cognitive Threat Intelligence and Explainable Federated Security Analytics for Distributed Infrastructure Systems

The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things IoT technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion...

From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation

Machine-learning-based Intrusion Detection Systems IDS have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes...

Convolutional-Neural-Networks for Deanonymisation of I2P Traffic

This study investigates the potential for deanonymizing services within the Invisible Internet Project I2P network through passive traffic analysis and machine learning techniques. The primary objective is to identify distinctive patterns in I2P traffic despite the encryption of its payload. To...

EDySec: A Deep Learning-Based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning ML detectors struggle to detect these...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

EUVD-2025-209448

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

PT-2026-32627

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

A side-channel vulnerability exists in the BIP-39 mnemonic processing implementation of Trezor One (v1.13.0 to v1.14.0), Trezor T (v1.13.0 to v1.14.0), and Trezor Safe (v1.13.0 to v1.14.0) hardware wallets. The root cause is derived from BIP-39 standard guidelines that cause non-constant time exe...