Proceedings ArticleDOI
Analyzing Intrusion Detection System: An ensemble based stacking approach
Sanjiban Sekhar Roy,P. Venkata Krishna,Sumanth Yenduri +2 more
- pp 000307-000309
TLDR
An IDS model, which classifies different types of intrusion attacks based on Stacking classifier, which has achieved good accuracy while classifying the KDD-Cup 99 dataset and that has been achieved with 10 fold cross validation.Abstract:
Intrusion Detection System (IDS) is an application software which detects the presence of hostile or intrusive elements inside the system. As the nature and type of the intrusions are continuously changing, a simple IDS cannot completely tackle the security threat. In this paper, we have proposed an IDS model, which classifies different types of intrusion attacks based on Stacking classifier. Stacking is an ensemble based classifier. We have achieved good accuracy while classifying the KDD-Cup 99 dataset and that has been achieved with 10 fold cross validation.read more
Citations
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Journal ArticleDOI
Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network
TL;DR: The proposed novel intrusion detection model that combines an improved conditional variational AutoEncoder with a deep neural network (DNN), namely ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation and shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.
Journal ArticleDOI
Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks
TL;DR: This paper proposes a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs) that achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods.
Journal ArticleDOI
An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach
Sapna Singh Kshatri,Deepak Singh,Bhavana Narain,Surbhi Bhatia,Mohammad Tabrez Quasim,G. R. Sinha +5 more
TL;DR: In this paper, an ensemble learning method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB.
Book ChapterDOI
Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree
TL;DR: Evaluation of the prediction models indicates that the Multivariate Adaptive Regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and Classification and Regression Tree.
References
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Journal ArticleDOI
Original Contribution: Stacked generalization
TL;DR: The conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate.
Journal ArticleDOI
A hybrid machine learning approach to network anomaly detection
Taeshik Shon,Jongsub Moon +1 more
TL;DR: A new SVM approach is proposed, named Enhanced SVM, which combines these two methods in order to provide unsupervised learning and low false alarm capability, similar to that of a supervised S VM approach.
Journal ArticleDOI
An efficient intrusion detection system based on support vector machines and gradually feature removal method
TL;DR: With the combination of clustering method, ant colony algorithm and support vector machine, an efficient and reliable classifier is developed to judge a network visit to be normal or not.