Jammer-Loc

Jammer-Loc 本存储库包含论文的代码和数据集链接 “用于室内 UWB 干扰源定位的机器与深度学习方法。” 使用示例 运行超参数优化过程： python hpo.py \ --task classification \ --mode actual \ --framework ml \ --config config.yaml \ --trials 50 \ --study-name mystudy 1. 模型和指标必须由 main.py 以预期格式保存。 2. Optuna 结果默认保存到 hpo.db。 3. 所有命令行选项均在 hpo.py 中使用 argparse...

PassGAN

PassGAN 本仓库包含论文 PassGAN: A Deep Learning Approach for Password Guessing 的代码。 PassGAN 模型取自 Improved Training of Wasserstein GANs，并假设 PassGAN 的作者在其工作中使用了 improvedwgantraining 的 TensorFlow 实现。因此，我在本仓库中对参考实现进行了修改，使其易于训练（train.py）和采样（sample.py）。本仓库贡献了： 命令行界面 基于 RockYou 数据集预训练的 PassGAN 模型 快速开始 需要预先安装...

deep-loglizer

Deep-loglizer Deep-loglizer es un conjunto de herramientas de análisis de registros logs basado en aprendizaje profundo para la detección automatizada de anomalías. Si utilizas deep-loglizer en tu investigación para publicación, por favor cita el siguiente artículo: Zhuangbin Chen, Jinyang Liu,...

degas

Degas 使用深度学习模型检测 DGA 生成的域名 运行 我目前使用 Conda（Anaconda/Miniconda）进行开发，但使用附带的 requirements.txt 时，你也可以使用 Pipenv 或 virtualenv。 conda: conda env create -f environment.yml conda activate degas Pipenv: pipenv install -r requirements.txt pipenv shell Virtualenv 类似，但确实再没有理由用 virtualenv 而不是 Pipenv 了。 重新训练模型...

DeepTraffic

用于网络流量分类的深度学习模型 更多信息请阅读我们的论文。 🎓王伟的谷歌学术主页 王伟, 曾学文, 叶晓舟, 盛益强, 朱明,"基于卷积神经网络的表示学习的恶意软件流量分类", 第31届国际信息网络会议ICOIN 2017, 第712-717页, 2017. 王伟, 王劲林, 曾学文, 杨中臻, 朱明,"基于一维卷积神经网络的端到端加密流量分类", 第15届IEEE国际智能与安全信息学会议IEEE ISI 2017, 第43-48页, 2017. 王伟, 盛益强, 王劲林, 曾学文, 叶晓舟, 黄永忠, 朱明,"HAST-IDS: 利用深度神经网络学习层次时空特征以改进入侵检测", IE...

SEVulDet

SEVulDet SEVulDet 是一个语义增强的基于深度学习的框架，能够通过提取、保留和学习更多语义来精确识别漏洞模式。 联系邮箱 ：[email protected] SEVulDet 详情 近年来，基于深度学习的漏洞检测框架受到更多关注，这些框架利用神经网络来识别漏洞模式。尽管已付出大量努力，但现有方法在实际应用中仍不够精确。先前的工作未能从源代码中全面捕获语义，或者采用了不合适的神经网络设计。 本文提出 SEVulDet，一个语义增强的基于深度学习的框架，通过提取、保留和学习更多语义来精确识别漏洞模式。首先，SEVulDet...

deep-pwning

Deep-pwning 是一个轻量级的框架，用于对机器学习模型进行实验，目标是评估它们在面对有动机的对手时的鲁棒性。 请注意，当前状态的 deep-pwning 远未 达到成熟或完成。它旨在供您实验、扩展和延伸。只有这样，我们才能真正帮助它成为 统计机器学习模型的渗透测试工具包 。 背景 研究人员发现，让机器学习模型（分类器、聚类器、回归器等）做出客观上错误的决策是出乎意料地容易。这一研究领域被称为...

awesome-latency-attacks

Awesome Deep Learning Latency Attacks & Defenses Este repositorio se mantiene como recurso complementario para el survey "Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey." Indexa artículos, enlaces a código, notas de taxonomía y figuras sobre amenazas de disponibilidad orientada...

PrivacyRaven

Note: This project is on hiatus. PrivacyRaven is a privacy testing library for deep learning systems. You can use it to determine the susceptibility of a model to different privacy attacks; evaluate privacy preserving machine learning techniques; develop novel privacy metrics and attacks; and...

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...