Distributed Learning Mechanism Against Flooding Network Attacks

Adaptive techniques based on machine learning and data mining are gaining relevance in selfmanagement and self-defense for networks and distributed systems. In this paper, we focus on early detection and stopping of distributed flooding attacks and network abuses. We extend the framework proposed by Zhang and Parashar (2006) to cooperatively detect and react to abnormal behaviors before the target machine collapses and network performance degrades. In this framework, nodes in an intermediate network share information about their local traffic observations, improving their global traffic perspective. In our proposal, we add to each node the ability of learning independently, therefore reacting differently according to its situation in the network and local traffic conditions. In particular, this frees the administrator from having to guess and manually set the parameters distinguishing attacks from non-attacks: now such thresholds are learned and set from experience or past data. We expect that our framework provides a faster detection and more accuracy in front of distributed flooding attacks than if static filters or single-machine adaptive mechanisms are used. We show simulations where indeed we observe a high rate of stopped attacks with minimum disturbance to the legitimate users.