Cidon's research is in two main areas: distributed storage systems and security.
Web-scale applications, such as social networks, Internet of Things and self-driving cars, heavily rely on fast access to vast amounts of data to power their machine learning and analytics algorithms. This data is stored on distributed storage systems, which need to provide reliability and performance at a very large scale (tens of thousands of servers or more), at a low cost of ownership. The main areas Cidon has focused on in his distributed storage research are: (1) how to provide reliability for distributed storage systems at scale, (2) how to design these systems on emerging storage technologies such as persistent memory and high-density flash, and (3) using machine learning to automatically tune storage system performance. His research on distributed storage was adopted in commercial storage systems of several companies, including Facebook, Tibco, Hortonworks and Rubrik.
In addition, Cidon's second main research thrust is cyber security, focusing on applying machine learning for detecting fraud and social engineering. His research methodology consists of finding anomalies in real-world data, and building practical security systems that can automatically detect these anomalies. His paper on characterizing detecting lateral phishing attacks recently won the Distinguished Paper Award at Usenix Security 2019, and his work on detecting email-based social engineering was deployed in production at Barracuda Networks.
Cidon joins the EE Department after serving as the Senior Vice President of Email Protection at Barracuda Networks. In that capacity, he co-led a team of 100 engineers and product managers and was responsible for $200M a year in revenue. He joined Barracuda Networks via acquisition of his startup, Sookasa, where he was the CEO and co-founder. Cidon obtained his PhD and MS in Electrical Engineering from Stanford and BS in Computer and Software Engineering from the Technion.