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Following the current big data trend, the scale of real-time system calltraces generated by Linux applications in a contemporary data center mayincrease excessively. Due to the deficiency of scalability, it is challengingfor traditional host-based intrusion detection systems deployed on every singlehost to collect, maintain, and manipulate those large-scale accumulated systemcall traces. It is inflexible to build data mining models on one physical hostthat has static computing capability and limited storage capacity. To addressthis issue, we propose SCADS, a corresponding solution using Apache Spark inthe Google cloud environment. A set of Spark algorithms are developed toachieve the computational scalability. The experiment results demonstrate thatthe efficiency of intrusion detection can be enhanced, which indicates that theproposed method can apply to the design of next-generation host-based intrusiondetection systems with system calls.

SCADS: A Scalable Approach Using Spark in Cloud for Host-based Intrusion Detection System with System Calls