Difference between revisions of "Cybersecurity"
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* [[Government Services]] | * [[Government Services]] | ||
* [[Cybersecurity: Evaluating & Selling]] | * [[Cybersecurity: Evaluating & Selling]] | ||
| + | * [[Watch me Build a Cybersecurity Startup]] | ||
* Useful Models [[...find outliers]]: | * Useful Models [[...find outliers]]: | ||
**[[One-class Support Vector Machine (SVM)]] | **[[One-class Support Vector Machine (SVM)]] | ||
Revision as of 15:59, 2 September 2019
Youtube search... ...Google search
- Case Studies
- Capabilities
- Cybersecurity References
- Defense
- Offense - Adversarial Threats/Attacks
- Cybersecurity Frameworks, Architectures & Roadmaps
- Cybersecurity Companies/Products
- Cybersecurity: National Institute of Standards and Technology (NIST) & U.S. Department of Homeland Security (DHS)
- Government Services
- Cybersecurity: Evaluating & Selling
- Watch me Build a Cybersecurity Startup
- Useful Models ...find outliers:
- Detecting Malicious Requests with Keras & TensorFlow | Adam Kusey - Medium
- Best security software: How 12 cutting-edge tools tackle today's threats | CSO
- graphistry.com
- Intelligence Advanced Research Projects Activity (IARPA)Is Trying Keep Adversaries From Corrupting AI Tools ... Could cyber adversaries be training the government’s artificial intelligence tools to fail? | Jack Corrigan - Nextgov
- TrojAI - Office of the Director of National Intelligence Office: Intelligence Advanced Research Projects Activity FedBizOpps.gov predict whether AI systems have been corrupted through so-called “Trojan attacks.”
- Adversarial Attacks on Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
- Breaking Down the Tencent 2018 Cybersecurity Report
- Chronicle combines all the best parts of Google and X culture
Fraud Detection
- Introduction to Fraud Detection Systems | Miguel Gonzalez-Fierro, Microsoft
- AI for Health Insurance Fraud Detection – Current Applications | Niccolo Mejia
