Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Data Mining Methods and Models
Data Mining Methods and Models
A Hybrid Model for Immune Inspired Network Intrusion Detection
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Network Anomaly Detection System: The State of Art of Network Behaviour Analysis
ICHIT '08 Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology
Security in Computing Systems: Challenges, Approaches and Solutions
Security in Computing Systems: Challenges, Approaches and Solutions
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Hybrid Model Based on Artificial Immune System and PCA Neural Networks for Intrusion Detection
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01
Study of neural network technologies in intrusion detection systems
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Use of dimensionality reduction for intrusion detection
ICISS'07 Proceedings of the 3rd international conference on Information systems security
Impact of Feature Reduction on the Efficiency of Wireless Intrusion Detection Systems
IEEE Transactions on Parallel and Distributed Systems
Architecture of distributed intrusion detection system based on anomalies
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
Prediction-oriented dimensionality reduction of industrial data sets
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Random-Forests-Based Network Intrusion Detection Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
| Hi-index | 0.00 |
The ever growing volume of network traffic results in the need for even more efficient data processing in Intrusion Detection Systems. In particular, the raw network data has to be transformed and largely reduced to be processed by data mining models. The primary objective of this work is to control the dimensionality reduction (DR) of network flow records in view of the accuracy of misuse detection. A real data set, containing flow records with potential spam messages, is used to perform the tests of the proposed method. The algorithm proposed in this study is applied to investigate the merits of hybrid models composed of dimensionality reduction, neural networks, and decision trees. The benefits of dimensionality reduction and the impact of the process on the overall spam detection rates and false positive rates are investigated. The advantages of the proposed technique over standard a priori selection of reduced dimension are discussed.