A privacy-preserving distributed and incremental learning method for intrusion detection

Authors:
Bertha Guijarro-Berdiñas;Santiago Fernandez-Lorenzo;Noelia Sánchez-Maroño;Óscar Fontenla-Romero
Affiliations:
Faculty of Informatics, University of A Coruña, Spain;Faculty of Informatics, University of A Coruña, Spain;Faculty of Informatics, University of A Coruña, Spain;Faculty of Informatics, University of A Coruña, Spain
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

Citing 7
Cited 0

Improving intrusion detection performance using keyword selection and neural networks

Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
A global optimum approach for one-layer neural networks

Neural Computation
Results of the KDD'99 classifier learning

ACM SIGKDD Explorations Newsletter
Consistency-based search in feature selection

Artificial Intelligence
Privacy-Preserving Distributed Learning Based on Genetic Algorithms and Artificial Neural Networks

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Combining Feature Selection and Local Modelling in the KDD Cup 99 Dataset

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A combination of discretization and filter methods for improving classification performance in KDD Cup 99 dataset

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Computer systems are facing an increased number of security threats, specially regarding Intrusion detection (ID). From the point of view of Machine learning, ID presents many of the new cutting-edge challenges: tackle with massive databases, distributed learning and privacypreserving classification. In this work, a new approach for ID capable of dealing with these problems is presented using the KDDCup99 dataset as a benchmark, where data have to be classified to detect an attack. The method uses Artificial Neural Networks with incremental learning capability, Genetic Algorithms and a feature selection method to determine relevant inputs. As supported by the experimental results, this method is able to rapidly obtain an accurate model based on the information of distributed databases without exchanging any compromised data, obtaining similar results compared with other authors but offering features that make the proposed approach more suitable for an ID application.