DeepTraffic

Deep Learning models for network traffic classification For more information please read our papers. 🎓Wei Wang's Google Scholar Homepage Wei Wang, Xuewen Zeng, Xiaozhou Ye, Yiqiang Sheng and Ming Zhu,"Malware Traffic Classification Using Convolutional Neural Networks for Representation Learning,"...

Jammer-Loc

Jammer-Loc This repository includes the code and the Dataset Link for the paper “Machine and Deep Learning for Indoor UWB Jammer Localization.” Usage Example Run the hyperparameter optimization process: root@kitploit: python hpo.py \ --task classification \ --mode actual \ --framework ml \ --conf...

deep-pwning

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PassGAN

PassGAN This repository contains code for the PassGAN: A Deep Learning Approach for Password Guessing paper. The model from PassGAN is taken from Improved Training of Wasserstein GANs and it is assumed that the authors of PassGAN used the improvedwgantraining tensorflow implementation in their...

SEVulDet

SEVulDet SEVulDet is a semantics-enhanced deep learning-based framework that can accurately pinpoint vulnerability patterns by extracting, preserving, and learning more semantics. E-mail for communication: [email protected] Details of SEVulDet Recent years have seen increased attention to de...

degas

Degas DGA-generated domain detection using deep learning models Running I'm currently using Conda Anaconda/Miniconda for development, but you should be able to use Pipenv or virtualenv as well using the included requirements.txt. conda: root@kitploit: conda env create -f environment.yml conda...

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...

deep-loglizer

Deep-loglizer Deep-loglizer is a deep learning-based log analysis toolkit for automated anomaly detection. If you use deep-loglizer in your research for publication, please kindly cite the following paper: Zhuangbin Chen, Jinyang Liu, Wenwei Gu, Yuxin Su, and Michael R. Lyu. Experience Report: De...

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

CVE-2025-69893 describes a side-channel vulnerability in BIP-39 mnemonic processing observed in Trezor hardware wallets (One v1.13.0–v1.14.0, T v1.13.0–v1.14.0, Safe v1.13.0–v1.14.0). The root cause is non-constant time execution and specific branch patterns during word search dictated by the BIP...

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...

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...