Main Article Content
Abstract
Distributed Denial of Service (DDOS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. softwarebased traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDOS attacks. Distributed denial-of-service (DDOS) attacks remain a major security problem, the mitigation of which is very hard especially when it comes to highly distributed bonnet-based attacks. The early discovery of these attacks, although challenging, is necessary to protect end-users as well as the expensive network infrastructure resources. In this thesis, we address the problem of DDOS attacks and present the theoretical foundation, architecture, and algorithms of Fiercely. The core of Fiercely is composed of intrusion prevention systems (IPSs) located at the Internet service providers (ISPs) level. The IPSs form virtual protection rings around the hosts to defend and collaborate by exchanging selected traffic information. The evaluation of Fiercely using extensive simulations and a real dataset is presented, showing Fiercely effectiveness and low overhead, as well as its support for incremental deployment in real networks. Load balancing is one of the main challenges, important technique, critical issue and play an important role which is required to distribute workload or task equally across the nodes or servers. and also this thesis address and provides a detailed summary of the load balancing optimization techniques of evolutionary and swarm based algorithms.