Editor's Note: The use of face recognition technology in policing has been a long-standing subject of concern, even more-so now after the murder of George Floyd and the demonstrations that have followed. In this article, Mike Loukides, VP of Content Strategy at O'Reilly Media, reviews how companies and cities have addressed these concerns, as well as ways in which individuals can mitigate face recognition technology or even use it to increase accountability. We'd love to hear from you about what you think about this piece. Largely on the impetus of the Black Lives Matter movement, the public's response to the murder of George Floyd, and the subsequent demonstrations, we've seen increased concern about the use of facial identification in policing. First, in a highly publicized wave of announcements, IBM, Microsoft, and Amazon have announced that they will not sell face recognition technology to police forces.

In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both trained on sequences of API calls. We contrast our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in contrasting our two dynamic analysis techniques, we find that using PHMMs consistently outperforms our analysis based on HMMs.