Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Computational complexity and the genetic algorithm
Computational complexity and the genetic algorithm
Feature selection of intrusion detection data using a hybrid genetic algorithm/KNN approach
Design and application of hybrid intelligent systems
CISC'05 Proceedings of the First SKLOIS conference on Information Security and Cryptology
Fusions of GA and SVM for anomaly detection in intrusion detection system
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Quantitative intrusion intensity assessment for intrusion detection systems
Security and Communication Networks
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Intrusion Detection System (IDS) should guarantee high detection rates with minimum overheads to figure out intrusion detection model and process audit data. The previous approaches have mainly focused on feature selection of audit data and parameters optimization of intrusion detection models. However, feature selection and parameters optimization have been performed in separate way. Several hybrid approaches based on soft computing techniques are able to perform both of them together but they have more computational overheads. In this paper, we propose a new approach named Simultaneous Intrinsic Model Identification (SIMI), which enable one to perform both feature selection and parameters optimization together without any additional computational overheads. SIMI adopts Random Forest (RF) which is a promising machine learning algorithm and has been shown similar or better classification rates compared to Support Vector Machines (SVM). SIMI is able to model lightweight intrinsic intrusion detection model with optimized parameters and features. After determination of the intrinsic intrusion detection model, we visualize normal and attack patterns in 2 dimensional space using Multidimensional Scaling (MDS). We carry out several experiments on KDD 1999 intrusion detection dataset and validate the feasibility of our approach.