Toward Network-based DDoS Detection in Software-defined Networks

To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress

Bandwidth management in live virtual machine migration

In this thesis I investigated the bandwidth management problem on live migration of virtual machine in different environment. First part of the thesis is dedicated to intra-data-center bandwidth optimization problem, while in the second part of the document I present the solution for wireless live migration in 5G and edge computing emerging technologies. Live virtual machine migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized data centers, so to lead to reduced energy consumption and improve data centers’ flexibility. However, the bandwidth consumption and latency of current state-of-the-art live VM migration techniques still reduce the experienced benefits to much less than their potential. Motivated by this consideration I analytically characterize and test the optimal bandwidth manager for intra-data-center live migration of VMs. The goal is to min- imize the migration-induced communication energy consumption under service level agreement (SLA)-induced hard constraints on the total migration time, downtime, slowdown of the migrating applications and overall available bandwidth