Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
On the use of computational geometry to detect software faults at runtime
Proceedings of the 7th international conference on Autonomic computing
Inoculation against malware infection using kernel-level software sensors
Proceedings of the 8th ACM international conference on Autonomic computing
On the Value of Coordination in Distributed Self-Adaptation of Intrusion Detection System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A survey of formal methods in self-adaptive systems
Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
VESPA: multi-layered self-protection for cloud resources
Proceedings of the 9th international conference on Autonomic computing
A Systematic Survey of Self-Protecting Software Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section on Best Papers from SEAMS 2012
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As the deployment of network-centric systems increases, network attacks are proportionally increasing in intensity as well as complexity. Attack detection techniques can be broadly classified as being signature-based, classification-based, or anomaly-based. In this paper we present a multi level intrusion detection system (ML-IDS) that uses autonomic computing to automate the control and management of ML-IDS. This automation allows ML-IDS to detect network attacks and proactively protect against them. ML-IDS inspects and analyzes network traffic using three levels of granularities (traffic flow, packet header, and payload), and employs an efficient fusion decision algorithm to improve the overall detection rate and minimize the occurrence of false alarms. We have individually evaluated each of our approaches against a wide range of network attacks, and then compared the results of these approaches with the results of the combined decision fusion algorithm.