Threat Surface Pulse
Real-time snapshots from CISA KEV and other signals. Highlights exposed risk and trending CVEs.
- Recent KEV additions
- Exec-ready talking points
Fortinet
Fortinet FortiFone, FortiVoice, FortiNDR and FortiMail contain a stack-based overflow vulnerability that may allow a remote unauthenticated attacker to execute arbitrary code or commands via crafted HTTP requests.
Microsoft
Microsoft Windows Ancillary Function Driver for WinSock contains a use-after-free vulnerability that allows an authorized attacker to escalate privileges to administrator.
Microsoft
Microsoft Windows Scripting Engine contains a type confusion vulnerability that allows an unauthorized attacker to execute code over a network via a specially crafted URL.
Microsoft
Microsoft Windows Common Log File System (CLFS) Driver contains a heap-based buffer overflow vulnerability that allows an authorized attacker to elevate privileges locally.
Microsoft
Microsoft Windows Common Log File System (CLFS) Driver contains a use-after-free vulnerability that allows an authorized attacker to elevate privileges locally.
AI/ML Signal Tracker
Tracks model releases, repos, and outages; summarizes impact for platform roadmaps.
- Top moving repos
- Signal strength
RepiFahmiSidiq/Onchain-Security-Suite
🛡️ Strengthen Web3 security with our AI-driven token auditor and reputation engine, ensuring safer transactions and reliable smart contracts.
mikehubers/Awesome-AI-For-Security
🛡️ Discover essential tools and resources that leverage AI for enhancing cybersecurity, focusing on modern technologies and their applications in security operations.
zimingttkx/Network-Security-Based-On-ML
🛡️ 基于机器学习的网络安全威胁检测系统 | 完整的端到端ML项目，包含数据处理、模型训练、Web界面和API服务 | 适合初学者学习的实战项目 | Python + FastAPI + Scikit-learn + XGBoost
Western-OC2-Lab/AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
prashantshukla01/Network_Security
This project aims to detect malicious network activity using Machine Learning-based Intrusion Detection. It focuses on analyzing network traffic data to classify whether behavior is normal or attack-related, helping organizations strengthen their cybersecurity posture.
PeterHovng/HUTECH_DACN.CyberSecurity
Đồ án chuyên ngành - ngành An ninh mạng "Hệ thống phát hiện tấn công mạng trên AWS bằng Machine Learning (Network Intrusion Detection System - NIDS)"