DEF CON · 2018DC26
Adversarial Patches
Sven Cattell
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DEF CON · 2018DC26
Adversarial Training
Zhao
DEF CON · 2018DC26
AI DevOps Behind the Scenes of a Global AV Company
Alex Long
DEF CON · 2018DC26
Automated Planning for the Automated Red Team
Andy Applebaum
DEF CON · 2018DC26
Beyond Adversarial Learning Security Risks in AI Implementations
Kang Li
DEF CON · 2018DC26
Boosting Adversarial Attacks with Momentum
Xingxing Wei
DEF CON · 2018DC26
Chatting with Your Programs to Find Vulnerabilities
Chris Gardner
DEF CON · 2018DC26
DeepPhish Simulating the Malicious Use of AI
Ivan Torroledo
DEF CON · 2018DC26
Detecting Web Attacks with Recurrent Neural Networks
Fedor Sakharov
DEF CON · 2018DC26
Generating Labeled Data From Adversary Sims with MITRE ATT&CK
Brian Genz
DEF CON · 2018DC26
Hardware Trojan Attacks on Neural Networks
Joseph Clements
DEF CON · 2018DC26
How to Leverage the Open Source Information
Li · Jiao
DEF CON · 2018DC26
Hunting the Ethereum Smart Contract Color Inspired Inspection
TonTon Huang
DEF CON · 2018DC26
Identifying and Correlating Anomalies in Internetwide Scan
Andrew Morris
DEF CON · 2018DC26
It is a Beautiful Day in the Malware Neighborhood
Matt
DEF CON · 2018DC26
JMPgate Accelerating Reverse Engineering Into Hyperspace
Rob Brandon
DEF CON · 2018DC26
Machine Learning Model Hardening For Fun and Profit
Ariel Herbert Voss
DEF CON · 2018DC26
Malware Panel
drhyrum
DEF CON · 2018DC26
Practical Adversarial Attacks
Moustafa Alzantot · Yash Sharma
DEF CON · 2018DC26
Rapid Anomaly Detection via Ransom Note File Classification
Mark Mager
DEF CON · 2018DC26
Recent Progress in Adversarial Deep Learning
Guo · Cuevas
DEF CON · 2018DC26
Responsible Offensive Machine Learning
bodaceacat
DEF CON · 2018DC26
StuxNNet Practical Live Memory Attacks on Machine Learning
Raphael Norwitz
DEF CON · 2018DC26
Targeted Adversarial Examples for Black Box Audio
Rohan Taori · Ameya Kamsetty