What is the current state of the art for Machine Learning in Malware Classification? This panel will dive into following topic areas: classification is one of the most successful uses of Machine Learning in the InfoSec industry.What does “Max Evil” in machine learning look like, really? What ethical boundaries and limitations exist in researching and implementing offensive use cases? Which holes in machine learning systems create the most incidental damage? And what can be done about all this? Machine Learning and Malware Analysis Panel Not all ML abuse looks like Terminators, but also DeepFakes, political impersonation, and distracting autonomous cars. evil hacks using machine learning are exploding in popularity.Panels (Responsible?) Offensive Machine Learning Machine Learning as a Service in Your Pocket Towards a framework to quantitatively assess AI safety – challenges, open questions and opportunities. GAN to the dark side: A case study of attacking machine-learning systems to empower defenses The great power of AI: Algorithmic mirrors of society Machine Learning Model Hardening For Fun and Profit Identifying and correlating anomalies in Internet-wide scan traffic to newsworthy security events Stop and Step Away from the Data: Rapid Anomaly Detection via Ransom Note File Classification JMPgate: Accelerating reverse engineering into hyperspace using AIĪutomated Planning for the Automated Red Team Generating Labeled Data From Adversary Simulations With MITRE ATT&CK Hunting the Ethereum Smart Contract: Color-inspired Inspection of Potential Attacksīeyond Adversarial Learning – Security Risks in AI ImplementationsĭeepPhish: Simulating the Malicious Use of AIĪI DevOps: Behind the Scenes of a Global Anti-Virus Company’s Machine Learning Infrastructure IntelliAV: Building an Effective On-Device Android Malware DetectorĬhatting with your programs to find vulnerabilities It’s a Beautiful Day in the Malware Neighborhood Machine Learning for Network Security Hands-on Workshop: DIYMLĭetecting Web Attacks with Recurrent Neural Networks Holy BATSense! Deploying TBATS Machine Learning Algorithm to Detect Security Events StuxNNet: Practical Live Memory Attacks on Machine Learning Systems The current state of adversarial machine learning DEF CON 26 is only one month away! We have a large number of amazing talks planned for everyone in attendance: Full List Title
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