Selfish MAC Layer Misbehavior in Wireless Networks
IEEE Transactions on Mobile Computing
Alert confidence fusion in intrusion detection systems with extended Dempster-Shafer theory
Proceedings of the 43rd annual Southeast regional conference - Volume 2
Bayesian Networks for Knowledge-Based Authentication
IEEE Transactions on Knowledge and Data Engineering
Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes
IEEE Transactions on Dependable and Secure Computing
REFACING: An autonomic approach to network security based on multidimensional trustworthiness
Computer Networks: The International Journal of Computer and Telecommunications Networking
Alert correlation by a retrospective method
ICOIN'09 Proceedings of the 23rd international conference on Information Networking
Discovery and prevention of attack episodes by frequent episodes mining and finite state machines
Journal of Network and Computer Applications
Detection of jamming attacks in wireless ad hoc networks using error distribution
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Detecting greedy behaviors by linear regression in wireless ad hoc networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Change-point detection for black-box services
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
A new data fusion model of intrusion Detection-IDSFP
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
D-S evidence theory and its data fusion application in intrusion detection
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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In computer and network security, standard approaches to intrusion detection and response attempt to detect and prevent individual attacks. However, it is not the attack but rather the attacker against which our networks must be defended. To do this, the information that is being provided by intrusion detection systems (IDS) must be gathered and then divided into its component parts such that the activity of individual attackers is made clear. Our approach to this involves the application of Bayesian methods to data being gathered from distributed IDS. With this we hope to improve the capabilities for early detection of distributed attacks against infrastructure and the detection of the preliminary phases of distributed denial of service attacks.