A Big Data and machine learning approach for network monitoring and security

Abstract

In the last decade the performances of 802.11 (Wi‐Fi) devices skyrocketed. Today it is possible to realize gigabit wireless links spanning across kilometers at a fraction of the cost of the wired equivalent. In the same period, mesh network evolved from being experimental tools confined into university labs, to systems running in several real world scenarios. Mesh networks can now provide city‐wide coverage and can compete on the market of Internet access. Yet, being wireless distributed networks, mesh networks are still hard to maintain and monitor. This paper explains how today we can perform monitoring, anomaly detection and root cause analysis in mesh networks using Big Data techniques. It first describes the architecture of a modern mesh network, it justifies the use of Big Data techniques and provides a design for the storage and analysis of Big Data produced by a large‐scale mesh network. While proposing a generic infrastructure, we focus on its application in the security domain.

Predictive machine learning-based integrated approach for DDoS detection and prevention

“…It is used to extract knowledge and relative patterns from the large dataset for predictive analysis [11,28,33]. The tremendous volume of existing and recently appearing network data influenced researchers to use data mining technologies to investigate attacks [16,17,21,23,32]. Currently, various researchers are continuing to develop an effective IDS system for DDoS using a machine learning algorithm [1,3,6,8,12,19,22,30].…”