An effective intrusion detection system using homogeneous ensemble techniques

In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.

Building heterogeneous ensembles by pooling homogeneous ensembles

Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. The proposed heterogeneous ensemble building strategy, composed of neural networks, SVMs, and random trees (i.e. from a standard random forest), is analyzed in a comprehensive empirical analysis and compared to a benchmark of other heterogeneous and homogeneous ensembles. The achieved results illustrate the gains that can be achieved by the proposed ensemble creation method with respect to both homogeneous ensembles and to the tested heterogeneous building strategy at a fraction of the training cost.

Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection

Automatic anomaly detection monitoring plays a vital role in water utilities’ distribution systems to reduce the risk posed by unclean water to consumers. One of the major problems with anomaly detection is imbalanced datasets. Dynamic selection techniques combined with ensemble models have proven to be effective for imbalanced datasets classification tasks. In this paper, water quality anomaly detection is formulated as a classification problem in the presences of class imbalance. To tackle this problem, considering the asymmetry dataset distribution between the majority and minority classes, the performance of sixteen previously proposed single and static ensemble classification methods embedded with resampling strategies are first optimised and compared. After that, six dynamic selection techniques, namely, Modified Class Rank (Rank), Local Class Accuracy (LCA), Overall-Local Accuracy (OLA), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U) and Meta-Learning for Dynamic Ensemble Selection (META-DES) in combination with homogeneous and heterogeneous ensemble models and three SMOTE-based resampling algorithms (SMOTE, SMOTE+ENN and SMOTE+Tomek Links), and one missing data method (missForest) are proposed and evaluated. A binary real-world drinking-water quality anomaly detection dataset is utilised to evaluate the models. The experimental results obtained reveal all the models benefitting from the combined optimisation of both the classifiers and resampling methods. Considering the three performance measures (balanced accuracy, F-score and G-mean), the result also shows that the dynamic classifier selection (DCS) techniques, in particular, the missForest+SMOTE+RANK and missForest+SMOTE+OLA models based on homogeneous ensemble-bagging with decision tree as the base classifier, exhibited better performances in terms of balanced accuracy and G-mean, while the Bg+mF+SMENN+LCA model based on homogeneous ensemble-bagging with random forest has a better overall F1-measure in comparison to the other models.

Robustness and Predictive Performance of Homogeneous Ensemble Feature Selection in Text Classification

The use of ensemble paradigm with classifiers is a proven approach that involves combining the outcomes of several classifiers. It has recently been extrapolated to feature selection methods to find the most relevant features. Earlier, ensemble feature selection has been used in high dimensional, low sample size datasets like bioinformatics. To one's knowledge there is no such endeavor in the text classification domain. In this work, the ensemble feature selection using data perturbation in the text classification domain has been used with an aim to enhance predictability and stability. This approach involves application of the same feature selector to different perturbed versions of training data, obtaining different ranks for a feature. Previous works focus only on one of the metrics, that is, stability or accuracy. In this work, a combined framework is adopted that assesses both the predictability and stability of the feature selection method by using feature selection ensemble. This approach has been explored on univariate and multivariate feature selectors, using two rank aggregators.