Proceedings ArticleDOI
Network attacks identification using consistency based feature selection and self organizing maps
Zeon Trevor Fernando,I. Sumaiya Thaseen,Ch. Aswani Kumar +2 more
- pp 162-166
TLDR
An anomaly detection model is proposed by deploying consistency based feature selection, J48 decision tree and self organizing map (SOM), which has been carried on KDD99 data set and each of the features selected using the integrated mechanism has been able to identify the attacks in the data set.Abstract:
Anomaly detection is one of the major areas of research with the tremendous development of computer networks. Any intrusion detection model designed should have the ability to visualize high dimensional data with high processing and accurate detection rate. Integrated Intrusion detection models combine the advantage of low false positive rate and shorter detection time. Hence this paper proposes an anomaly detection model by deploying consistency based feature selection, J48 decision tree and self organizing map (SOM). Experimental analysis has been carried on KDD99 data set and each of the features selected using the integrated mechanism has been able to identify the attacks in the data set. Keywords— Self Organizing Map, Consistency based Feature Selection, Intrusion Detection Systems.read more
Citations
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Journal ArticleDOI
Novel feature selection and classification of Internet video traffic based on a hierarchical scheme
TL;DR: The experimental results show that the proposed method outperforms existing methods applying commonly used flow statistical features, and is found to be more effective in discriminating different video traffics, especially from the QoS perspective, than commonly used features available in the literature.
Proceedings ArticleDOI
Intrusion detection model using fusion of PCA and optimized SVM
TL;DR: A novel method of integrating principal component analysis (PCA) and support vector machine (SVM) by optimizing the kernel parameters using automatic parameter selection technique is proposed, which reduces the training and testing time to identify intrusions thereby improving the accuracy.
Journal ArticleDOI
A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection
TL;DR: By comparing with the two SOM-based intrusion detection systems, the overall goal of this survey is to comprehensively compare the primitive components and properties of SOM- based intrusion detection.
Journal ArticleDOI
Experimental Review of Neural-Based Approaches for Network Intrusion Management
TL;DR: An experimental-based review of neural-based methods applied to intrusion detection issues, including deep-based approaches or weightless neural networks, which feature surprising outcomes and quantifies the value of neural networks when state-of-the-art datasets are used to train the models.
Journal ArticleDOI
Experimental Review of Neural-based approaches for Network Intrusion Management
TL;DR: In this article, the authors provide an experimental-based review of neural-based methods applied to intrusion detection issues, including deep-based approaches or weightless neural networks, and evaluate novel datasets (updated w.r.t. the obsolete KDD99 set).
References
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Journal ArticleDOI
Consistency-based search in feature selection
Manoranjan Dash,Huan Liu +1 more
TL;DR: An empirical study is conducted to examine the pros and cons of these search methods, give some guidelines on choosing a search method, and compare the classifier error rates before and after feature selection.
Journal ArticleDOI
An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks
TL;DR: The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers.
Book ChapterDOI
Self-Organizing Map
T.-S. Chon,Y.-S. Park +1 more
TL;DR: The unsupervised learning process was applied to provide a comprehensive view on ecological data through the use of ordination and classification to reveal the adaptive convergence of connection weights among computation nodes (i.e., neurons).
Book ChapterDOI
Consistency Based Feature Selection
TL;DR: This work focuses on one measure called consistency, which is an effective technique in dealing with dimensionality reduction for classification task and its properties in comparison with other major measures and different ways of using this measure in search of feature subsets.