Change detection in temporally evolving computer networks: A big data framework

The focus of this paper is to utilize a big data framework to characterize the behavior of central nodes over time in order to detect changes in large evolving computer networks. Changes in large evolving networks may indicate potential events such as cyber attacks, network failures or major shifts in network usage due to current events. Our approach entails the use of big data processing techniques such as MapReduce, to monitor central nodes to determine Consistency and Inconsistency (CoIn) in their availability and degree centrality across time periods. We also identify the Time Periods of Change (TPC) associated with CoIn. We present experimental results using real world internet traffic trace data, which indicates the potential of our approach to efficiently combine distributed processing techniques such as MapReduce to identify patterns of deviations.