Daniel Kats

Daniel Kats is a Principal Researcher, as well as an avid espresso drinker and pub quiz aficionado. His primary research is on the application of machine learning to creative tasks normally handled by humans, as well as human-machine collaboration. Daniel received a Masters in Computer Science from the University of Toronto in 2016, where he was advised by Eyal de Lara.

Daniel has authored a number of patents in diverse computer science areas including privacy, machine learning, server hardening, alert management, risk evaluation, identity, and malware detection. He has also published in the areas of virtualization systems, machine learning, and data visualization.

Selected Academic Papers

  • A Field Study of Computer-Security Perceptions Using Anti-Virus Customer-Support Chats
    Mahmood Sharif, Kevin A. Roundy, Matteo Dell'Amico, Christopher Gates, Daniel Kats, Lujo Bauer, Nicolas Christin
    In Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI 2019)

    To identify needs for improvement in security products, we study security concerns raised in Norton Security customer support chats. We found that many consumers face technical support scams and are susceptible to them. Findings also show the value of customer support centers in that 96% of customers that reach out for support in relation to scams have not paid the scammers.

  • Hierarchical Incident Clustering for Security Operation Centers
    David Silva, Matteo Dell’Amico, Michael Hart, Kevin A. Roundy, Daniel Kats
    In Proceedings of the Interactive Data Exploration and Analytics Workshop (IDEA 2018)

    We enable security incident responders to dispatch multiple similar security incidents at once through an intuitive user interface. The heart of our algorithm is a visualized hierarchical clustering technique that enables responders to identify the appropriate level of cluster granularity at which to dispatch multiple incidents.

  • Smoke Detector: Cross-Product Intrusion Detection With Weak Indicators
    Kevin A. Roundy, Acar Tamersoy, Michael Spertus, Michael Hart, Daniel Kats, Matteo Dell'Amico, Robert Scott
    In Proceedings of the Annual Computer Security Applications Conference (ACSAC 2017)

    Smoke Detector significantly expands upon limited collections of hand-labeled security incidents by framing event data as relationships between events and machines, and performing random walks to rank candidate security incidents. Smoke Detector significantly increases incident detection coverage for mature Managed Security Service Providers.