Constructing attack scenarios through correlation of intrusion alerts
Proceedings of the 9th ACM conference on Computer and communications security
Probabilistic Alert Correlation
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Measuring intrusion detection capability: an information-theoretic approach
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Modeling network intrusion detection alerts for correlation
ACM Transactions on Information and System Security (TISSEC)
Network Intrusion Detection System Using Neural Networks
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
A logic-based model to support alert correlation in intrusion detection
Information Fusion
Intrusion Detection Method Using Neural Networks Based on the Reduction of Characteristics
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Multilevel event correlation based on collaboration and temporal causal correlation
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Event Correlation on the Basis of Activation Patterns
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
An online adaptive approach to alert correlation
DIMVA'10 Proceedings of the 7th international conference on Detection of intrusions and malware, and vulnerability assessment
Hi-index | 0.00 |
The use of alert correlation methods in Distributed Intrusion Detection Systems (DIDS) has become an important process to address some of the current problems in this area. However, the efficiency obtained is far from optimal results. This paper presents a novel approach based on the integration of multiple correlation methods by using the neural network Growing Neural Gas (GNG). Moreover, since correlation systems have different detection capabilities, we have modified the learning algorithm to positively weight the best performing systems. The results show the validity of the proposal, both the multiple integration approach using GNG neural network and the weighting based on efficiency.