Ranjan Sinha

Ranjan Sinha

Ranjan Sinha is head of data science engineering & technology at eBay Inc., where he leads projects on customer analytics and personalization. He is a seasoned scientist, engineer, and technology innovator with a strong track record of accomplishments with over 10 years of experience leading innovative projects in industry and academia. Earlier, as lead data scientist at eBay, he led multiple business impacting projects across several domains, including recommendations and personalization, that have significantly enhanced consumers’ shopping experiences. He has also contributed to domains such as infrastructure availability, security, and identity linking.

Prior to joining eBay in 2010, Ranjan was a research scientist/academic at the University of Melbourne and holds a PhD in computer science from RMIT University, Australia. He has published over 30 refereed works, including in top-tier venues such as ACM SIGMOD, Bioinformatics Journal, IEEE Big Data, and VLDB journal. Ranjan won the sort benchmark medals for both JouleSort and PennySort and was amongst Wall Street Journal’s top-12 Asia-Pacific young inventors. He frequently speaks on data sciences, personalization, and big data technologies and has been an invited speaker at several venues including Google, Bell Labs, Innovation Summits, and Big Data conferences. He was a panelist at the 2015 IEEE Big Data conference, interviewed by KDnuggets, presented a tutorial on "E-commerce Personalization at Scale" at the 2014 ACM CIKM conference, and is a co-organizer of the popular Bay Area Search Meetup consisting of over 2,000 members.

Ranjan’s current interests include scaling data science solutions, engineering practices in data science pipelines, developing real-time predictive analytic solutions, and in the application of semantic relationships in large text corpus.

Santa Clara, Oct. 29 2015-Nov. 1 2015

Eagle: User Profile-based Anomaly Detection for Securing Hadoop Clusters

Chaitali Gupta, Ranjan Sinha, Yong Zhang

Existing Big data analytics platforms, such as Hadoop, lack support for user activity monitoring. Several diagnostic tools such as Ganglia, Ambari, and Cloudera Manager are available to monitor health of a cluster, however, they do not provide algorithms to detect security threats or perform user activity monitoring. Hence, there is a need to develop a scalable system that can detect malicious user activities, especially in real-time, so that appropriate actions can be taken against the user. At eBay, we developed such a system named Eagle, which collects audit logs from Hadoop clusters and applications running on them, analyzes users behavior, generates profiles per user of the system, and predicts anomalous user activities based on their prior profiles. Eagle is a highly scalable system, capable of monitoring multiple eBay clusters in real-time. It includes machine-learning algorithms that create user profiles based on the user's history of activities. As far as we know, this is the first activity monitoring system on the Hadoop-ecosystem for the detection of intrusion-related activities using behavior-based profiles of users. When a user performs any operation in the cluster, Eagle matches current user action against his prior activity pattern and raises alarm if it suspects anomalous action. We investigate two machine-learning algorithms: density estimation, and principal component analysis (PCA). In this paper, we introduce the Eagle system, discuss the algorithms in detail, and show performance results. We demonstrate that the sensitivity of the density estimation algorithm is 93%, however the sensitivity of our system increases by 4.94% (on average) to 98% (approximately) by using an ensemble of the two algorithms during anomaly detection.