abstract: Security has been one of the top concerns in cloud community while cloud resource abuse and malicious insiders are considered as top threats. Traditionally, Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) have been widely deployed to manipulate cloud security, with the latter one providing additional prevention capability. However, as one of the most creative networking technologies, Software-Defined Networking (SDN) is rarely used to implement IDPS in the cloud computing environment because the lack of comprehensive development framework and processing flow. Simply migration from traditional IDS/IPS systems to SDN environment are not effective enough for detecting and defending malicious attacks. Hence, in this thesis, we present an IPS development framework to help user easily design and implement their defensive systems in cloud system by SDN technology. This framework enables SDN approaches to enhance the system security and performance. A Traffic Information Platform (TIP) is proposed as the cornerstone with several upper layer security modules such as Detection, Analysis and Prevention components. Benefiting from the flexible, compatible and programmable features of SDN, Customized Detection Engine, Network Topology Finder, Source Tracer and further user-developed security appliances are plugged in our framework to construct a SDN-based defensive system. Two main categories Python-based APIs are designed to support developers for further development. This system is designed and implemented based on the POX controller and Open vSwitch in the cloud computing environment. The efficiency of this framework is demonstrated by a sample IPS implementation and the performance of our framework is also evaluated. / Dissertation/Thesis / Masters Thesis Computer Science 2014
| Identifer | oai:union.ndltd.org:asu.edu/item:25895 |
| Date | January 2014 |
| Contributors | Xiong, Zhengyang (Author), Huang, Dijiang (Advisor), Xue, Guoliang (Committee member), Dalvucu, Hasan (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 68 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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