B-dids: Mining anomalies in a Big-distributed Intrusion Detection System
Vandana Pursnani Janeja, Ali Azari, Josephine Namayanja, Brian Heilig · 2014
The focus of this paper is to present the architecture of a Big-distributed Intrusion Detection System (B-dIDS) to discover multi-pronged attacks which are anomalies existing across multiple subnets in a distributed network. The B-dIDS is composed of two key components: a big data processing engine and an analytics engine. The big data processing is done through HAMR, which is a next generation in-memory MapReduce engine. HAMR has reported high speedups over existing big data solutions across several analytics algorithms. The analytics engine comprises a novel ensemble algorithm, which extracts training data from clusters of the multiple IDS alarms. The clustering is utilized as a preprocessing step to re-label the datasets based on their high similarity to known potential attacks. The overall aim is to predict multi-pronged attacks that are spread across multiple subnets but can be missed if not evaluated in an integrated manner.