  With the advancing digitization of our society, network security has become
one of the critical concerns for most organizations. In this paper, we present
CopAS, a system targeted at Big Data forensics analysis, allowing network
operators to comfortably analyze and correlate large amounts of network data to
get insights about potentially malicious and suspicious events. We demonstrate
the practical usage of CopAS for insider attack detection on a publicly
available PCAP dataset and show how the system can be used to detect insiders
hiding their malicious activity in the large amounts of data streams generated
during the operations of an organization within the network.
