C4.5: programs for machine learning
C4.5: programs for machine learning
Testing and evaluating computer intrusion detection systems
Communications of the ACM
Template-based procedures for neural network interpretation
Neural Networks
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
ACM Transactions on Information and System Security (TISSEC)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Three learning phases for radial-basis-function networks
Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Mining intrusion detection alarms for actionable knowledge
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
Intrusion detection: modeling system state to detect and classify anomalous behaviors
Intrusion detection: modeling system state to detect and classify anomalous behaviors
Application of SVM and ANN for intrusion detection
Computers and Operations Research
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Network intrusion detection: Evaluating cluster, discriminant, and logit analysis
Information Sciences: an International Journal
Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes
IEEE Transactions on Dependable and Secure Computing
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fast learning in networks of locally-tuned processing units
Neural Computation
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
RAID '08 Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
Information Sciences: an International Journal
ICSNC '08 Proceedings of the 2008 Third International Conference on Systems and Networks Communications
Technical data mining with evolutionary radial basis function classifiers
Applied Soft Computing
Information Security and Cryptology --- ICISC 2008: 11th International Conference, Seoul, Korea, December 3-5, 2008, Revised Selected Papers
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fast and efficient training of RBF networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Intrusion detection in computer networks with neural and fuzzy classifiers
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Online Intrusion Alert Aggregation with Generative Data Stream Modeling
IEEE Transactions on Dependable and Secure Computing
Finding boundary subjects for medical decision support with support vector machines
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Learning intrusion detection: supervised or unsupervised?
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Mutual information-based feature selection for intrusion detection systems
Journal of Network and Computer Applications
Nonlinear mappings in problem solving and their PSO-based development
Information Sciences: an International Journal
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Techniques for knowledge acquisition in dynamically changing environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Using sensitivity analysis and visualization techniques to open black box data mining models
Information Sciences: an International Journal
Information Sciences: an International Journal
Opcode sequences as representation of executables for data-mining-based unknown malware detection
Information Sciences: an International Journal
Toward a more practical unsupervised anomaly detection system
Information Sciences: an International Journal
idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining
Information Sciences: an International Journal
Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
Information Sciences: an International Journal
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Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of ''certainty''. These properties support an analysis of radial basis function classifiers and allow for an adaption to ''novel'' kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.