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
SAP
SAP NetWeaver Visual Composer Metadata Uploader contains an unrestricted file upload vulnerability that allows an unauthenticated agent to upload potentially malicious executable binaries.
Broadcom
Broadcom Brocade Fabric OS contains a code injection vulnerability that allows a local user with administrative privileges to execute arbitrary code with full root privileges.
Qualitia
Qualitia Active! Mail contains a stack-based buffer overflow vulnerability that allows a remote, unauthenticated attacker to execute arbitrary or trigger a denial-of-service via a specially crafted request.
Commvault
Commvault Web Server contains an unspecified vulnerability that allows a remote, authenticated attacker to create and execute webshells.
Microsoft
Microsoft Windows NTLM contains an external control of file name or path vulnerability that allows an unauthorized attacker to perform spoofing over a network.
AI/ML Signal Tracker
Tracks model releases, repos, and outages; summarizes impact for platform roadmaps.
- Top moving repos
- Signal strength
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.
RepiFahmiSidiq/Onchain-Security-Suite
🛡️ Strengthen Web3 security with our AI-driven token auditor and reputation engine, ensuring safer transactions and reliable smart contracts.
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)"