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.
翻译:随着我们社会数字化进程的不断推进,网络安全已成为大多数组织关注的重点之一。本文介绍了 CopAS,一个用于大数据取证分析的系统,可以让网络操作员轻松分析和相互关联大量网络数据,以获取有关潜在的恶意和可疑事件的见解。我们演示了CopAS在一组公开的PCAP数据集上用于内部攻击检测的实际用途,并展示了该系统如何用于检测内部人员在组织网络运营期间在大量数据流中隐藏其恶意活动。