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Computer network defense relies heavily on human operators but we have failed to monitor, analyze, optimize, or mitigate issues related to physical and cognitive limits that may threaten human and mission safety and productivity. This is a research project to study the potential for increased human resiliency using risk-aware recommender systems that leverage machine power to aid human computer defense activities.
Recommender systems have become increasingly commonplace in everyday life, from movie and music to online dating. These systems use past behavior and other data sources to help the user make more informed, relevant, and timely decisions. Despite large quantities of similar data, however, cyber security and computer network defense have underutilized historical data to inform human decisions.
Consider this example. An analyst in the security operations center is investigating suspicious web requests that may indicate an attack against his corporate webserver. The analyst suspects that blocking the machine generating the suspicious traffic may mitigate the problem. Should he take the action? A recommender system might recommend a different course of action based on historical knowledge about the webserver, suspected attacker, or even world events.
Recommender systems for cyber defense are likely to differ from existing solutions and approaches. Unlike stable and predictable recommender systems like Netflix, cyber defense is incredibly dynamic and must consider both historic and real-time information when recommending a course of action. Another difference is that Netflix is primarily concerned with optimizing the accuracy and relevance of its recommendations. Recommendations in cyber defense must be not only accurate and relevant, but must consider a variety of user-defined or machine-inferred risks of accepting (or rejecting) a recommendation.
Even if new technology can produce a recommender system for network defense, there may still be challenges related to human usability and effectiveness. Research suggests that algorithm avoidance is a powerful detractor, and future work remains which considers human trust and acceptance of recommender systems in cyber security.
Example Approaches:
Research proposals should describe innovative approaches to recommender systems that measurably improve human satisfaction, productivity, and/or operational security in computer network defense. Proposals may incorporate multidisciplinary approaches and may consider:
How can a recommender system improve computer network defense?
What network defense data that is commonly collected today is valuable to the recommendation of future actions? What additional data, if collected, would measurably improve recommendations? How should that data be stored so that it can queried quickly, even as the volume and velocity of data increase?
What new or existing algorithms can incorporate historical and real-time data to produce accurate and relevant recommendations? Under what conditions or situations does an algorithm produce accurate and trustworthy recommendations?
Which methods and types of recommendation are most useful, compelling, and productive for human users? How do mental or behavioral limitations affect the utility and adoption of recommendation systems, and how can they be overcome?
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