The Effects of IDS/IPS Placement on Big Data Systems in Geo-Distributed Wide Area Networks

Geographically-distributed wide-area networks (WANs) offer expansive distributed and parallel computing capabilities. This includes the ability to advance Wide-Area Big Data (WABD). As data streaming traverses foreign networks, intrusion detection systems (IDSs) and intrusion prevention systems (IDSs) play an important role in securing information. The authors anticipate that securing WAN network topology with IDSs/IPSs can significantly impact wide-area data streaming performance. In this paper, the researchers develop and implement a geographically distributed big data streaming application using the Python programming language to benchmark IDS/IPS placement in hub-and-spoke, custom-mesh, and full-mesh network topologies. The results of the experiments illustrate that custom-mesh WANs allow IDS/IPS placements that maximize data stream packet transfers while reducing overall WAN latency. Hub-and-spoke network topology produces the lowest combined WAN latency over competing network designs but at the cost of single points of failure within the network. IDS/IPS placement in full-mesh designs is less efficient than custom-mesh yet offers the greatest opportunity for highly available data streams. Testing is limited by specific big data systems, WAN topologies, and IDS/IPS technology.