Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Intrusion Detection Based on the Immune Human System
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Detecting Anomalous and Unknown Intrusions Against Programs
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning techniques for an intrusion detection system
Proceedings of the 2004 ACM symposium on Applied computing
Network Intrusion Detection Using an Improved Competitive Learning Neural Network
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
Application of SVM and ANN for intrusion detection
Computers and Operations Research
Decision tree classifier for network intrusion detection with GA-based feature selection
Proceedings of the 43rd annual Southeast regional conference - Volume 2
A hierarchical SOM-based intrusion detection system
Engineering Applications of Artificial Intelligence
A New Variant of the Optimum-Path Forest Classifier
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Supervised pattern classification based on optimum-path forest
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Data clustering as an optimum-path forest problem with applications in image analysis
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
WMWA '09 Proceedings of the 2009 Second Pacific-Asia Conference on Web Mining and Web-based Application
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Music-Inspired Harmony Search Algorithm: Theory and Applications
Music-Inspired Harmony Search Algorithm: Theory and Applications
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
Decision tree based light weight intrusion detection using a wrapper approach
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
Learning classifiers for misuse detection using a bag of system calls representation
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In order to overcome such limitations, we have introduced a new pattern recognition technique called optimum-path forest (OPF) to this task. Our proposal is composed of three main contributions: to apply OPF for intrusion detection, to identify redundancy in some public datasets and also to perform feature selection over them. The experiments have been carried out on three datasets aiming to compare OPF against Support Vector Machines, Self Organizing Maps and a Bayesian classifier. We have showed that OPF has been the fastest classifier and the always one with the top results. Thus, it can be a suitable tool to detect intrusions on computer networks, as well as to allow the algorithm to learn new attacks faster than other techniques.