Machine Learning for Security and Security for Machine Learning with Nicole Nichols - TWiML Talk #252

This article summarizes a podcast episode featuring Nicole Nichols, a senior research scientist, discussing her presentation at GTC. The core focus is on the intersection of machine learning and security. The discussion covers two key use cases: insider threat detection and software fuzz testing. The article highlights the application of recurrent neural networks (RNNs), both standard and bidirectional, for identifying malicious activities. It also touches upon the use of deep learning to enhance software fuzzing techniques. The article promises a deeper dive into these topics, suggesting a practical application of AI in cybersecurity.

• •The presentation explores the use of machine learning to improve security.
• •Two use cases discussed are insider threat detection and software fuzz testing.
• •RNNs and deep learning are key technologies used in the discussed applications.