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
Microsoft
Microsoft Windows Management Console contains unspecified vulnerability that allows for remote code execution.
Qualcomm
Multiple Qualcomm chipsets contain a use-after-free vulnerability due to memory corruption in DSP Services while maintaining memory maps of HLOS memory.
Synacor
Synacor Zimbra Collaboration Suite (ZCS) contains an unspecified vulnerability in the postjournal service that may allow an unauthenticated user to execute commands.
Ivanti
Ivanti Endpoint Manager (EPM) contains a SQL injection vulnerability in Core server that allows an unauthenticated attacker within the same network to execute arbitrary code.
SAP
SAP Commerce Cloud (formerly known as Hybris) contains a deserialization of untrusted data vulnerability within the mediaconversion and virtualjdbc extension that allows for code injection.
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.AWS
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)"