Biblio

Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer and adaptive layer, is initiated at the data layer and the control layer of SDN, respectively. The proposed model is reactive and lightweight for DoS mitigation in SDNs.

Denial-of-Service (DoS) attacks pose a threat to any service provider on the internet. While traditional DoS flooding attacks require the attacker to control at least as much resources as the service provider in order to be effective, so-called low-rate DoS attacks can exploit weaknesses in careless design to effectively deny a service using minimal amounts of network traffic. This paper investigates one such weakness found within version 2.2 of the popular Apache HTTP Server software. The weakness concerns how the server handles the persistent connection feature in HTTP 1.1. An attack simulator exploiting this weakness has been developed and shown to be effective. The attack was then studied with spectral analysis for the purpose of examining how well the attack could be detected. Similar to other papers on spectral analysis of low-rate DoS attacks, the results show that disproportionate amounts of energy in the lower frequencies can be detected when the attack is present. However, by randomizing the attack pattern, an attacker can efficiently reduce this disproportion to a degree where it might be impossible to correctly identify an attack in a real world scenario.