Difference between revisions of "Cybersecurity"
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* Adversarial Attacks on [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]] | * Adversarial Attacks on [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]] | ||
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<youtube>cjo_u_yT2wQ</youtube> | <youtube>cjo_u_yT2wQ</youtube> | ||
<youtube>YiUN35Ikdfw</youtube> | <youtube>YiUN35Ikdfw</youtube> | ||
Revision as of 22:07, 26 January 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
- Useful Models ...find outliers:
- Best security software: How 12 cutting-edge tools tackle today's threats | CSO
- Introduction to Fraud Detection Systems | Miguel Gonzalez-Fierro, Microsoft
- 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
