This page serves as my blog. I typically write my blog posts on Medium and keep track of them here. I blog about topics related to my research, mainly security and deep learning. Feel free to reach out to me using the comments feature on Medium posts.
I also co-author a blog on the security and privacy of machine learning with Ian Goodfellow at www.cleverhans.io.
In this post, we’ll take adversarial examples as an illustration of why attacking machine learning seems easier than defending it. In other words, we cover in detail some of the reasons why we do not yet have completely effective defenses against adversarial examples, and we speculate about whether we can ever expect such a defense.
This blog post, jointly written with Ian Goodfellow, serves to introduce our new Clever Hans blog, in which we will discuss all of the many ways an attacker can break a machine learning algorithm.
This post is a short review of the paper by Roemer et al. published in ACM Transactions on Information and System Security in March 2012.
In this post, I describe a simple tutorial that allows you to train a simple decision tree classifier to detect websites used for phishing.
This post is a short review of the Kerberos article published in IEEE Communications in 1994 by B. Clifford Neuman and Theodore Ts’o.
Here are a few notes I jotted down during talks by Adrienne Porter Felt, Jon Oberheide, and Matthew Smith on the topic of usable security. These talks were part of Enigma, a conference launched this year by USENIX.
At Enigma 2016, Ron Rivest presented one of his papers that discusses the idea of providing “exceptional access” in encrypted systems to law enforcement. Rivest explained why he and his coauthors think exceptional access by law enforcement would cause great damage for society.
Enigma is a security conference launched this year by the USENIX association. Here are a few notes I jotted down during talks by Tadayoshi Kohno and Stefan Savage covering the security of Internet of Things (IoT).
Enigma is a security conference launched this year by the USENIX association. Here are a few notes I jotted down during talks by Avi Rubin and Kevin Fu covering the question of healthcare security.
This post provides a brief overview of Natural Language Processing. Its intent is not to exhaustively cover the field but rather to offer a collection of leads for additional reading. A key research area for human-computer interaction, Natural Language Processing is focused on the interaction between computers and natural languages spoken by humans to allow for computers to both understand and generate natural language. Natural Language Processing is increasingly related to Machine Learning as techniques are shifting from manually designing large sets of rules to inferring these rules from a large corpus of text.