Self-protecting and self-optimizing database systems: implementation and experimental evaluation
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
A model-based approach to self-protection in computing system
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
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With increasing faults and attacks on the Internet infrastructure, there is an impending need to provide automatic techniques to detect and mitigate the impact of attacks on network services. Denial-of-service attacks have been successful in denying legitimate traffic access to its required resources because existing routing protocols treat the attacking traffic equally as any normal traffic. This paper presents a proactive network defense framework that can be integrated with existing quality-of-service (QoS) protocols to provide differentiated services to network traffic flows based on their distance from the normal behavior. We introduce a new metric that we refer to as abnormality distance (AD) metric that can be used to classify traffic into normal, probable normal, probable abnormal (suspicious traffic), and abnormal (attacking traffic). The AD metric can then be used in conjunction with any QoS protocol to give high priority to normal traffic and lower priority to abnormal traffic. We demonstrate through several examples, how our approach can dynamically detect attacks, quantify their impact, and how to reduce the impacts and recover from them.