A Combination of PSO-Based Feature Selection and Tree-Based Classifiers Ensemble for Intrusion Detection Systems

Tama, Bayu Adhi; Rhee, Kyung Hyune

doi:10.1007/978-981-10-0281-6_71

Cited by 48 publications

(23 citation statements)

References 15 publications

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“…Choudhury and Bhowal builds several Boosting Ensemble for intrusion detection using of many Machine Learning Algorithms, and concluded that Random forest and Bayes Net are the two most suitable algorithms in terms of classification accuracy to build Intrusion Detection models [19]. [20] Proposed a Particle Swarm Optimization (PSO) for feature selection for an ensemble of three base classifiers; (Classification and Regression Tree -CART, Random Forest-RF and C4.5 Decision tree), the implementation of ensemble system showed a promising accuracy and lower false alarm rate than existing ensemble techniques. [21] compares the classification accuracy and false alarm rate performance improvement of bagging, boosting, and stacking approaches to the ensemble of intrusion detection models, Four base algorithms; Naïve Bayes, Decision tree, JRip (rule induction), and K-nearest neighbor was used to build the bagging and boosting ensembles, additionally, each of the four base models was used in turn to combine the predictions of the rest of the base-models, the stacked ensemble approach achieves the highest classification accuracy of more that 99% for known attacks and highest accuracy of 60% for unknown attacks than the bagging and boosting approach.…”

Section: Related Workmentioning

confidence: 99%

Multiple Model Tree Meta Algorithms Improvement of Network Intrusion Detection Predictions Accuracy

Olasehinde¹,

Olayemi²,

Alese³

2019

IJISR

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Security of Information is a critical issue for many organizations. Intrusion Detection systems (IDSs) protect information system by analyzing network packet to determine if it is abnormal or normal. This paper applies Multiple Model Trees (MMT) stacked ensemble algorithm to improve the classification accuracy of network intrusion. The predictions of the K Nearest Neighbor, Decision Tree and Naïve Bayes intrusion detection models built with UNSW-NB15 intrusion detection training dataset served as input to Multiple Model Tree (MMT)meta learner algorithm via a tenfold cross validation to build the MMT stacked ensemble model used for the final binary classifications of the network traffics (attacks and normal) and multi-class classification into any of the nine network attacks or normal. The evaluation of all models on the testing dataset results show that MMT algorithm improves the prediction accuracy of each of the three base machine learning model predictions, It recorded the highest classification accuracy of 97.93% and lowest false alarm rate of 0.22% for the binary classification and improves the multi-class classification accuracy of all the base models prediction

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Section: Related Workmentioning

confidence: 99%

Multiple Model Tree Meta Algorithms Improvement of Network Intrusion Detection Predictions Accuracy

Olasehinde¹,

Olayemi²,

Alese³

2019

IJISR

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“…It is used to search the set of all possible features so that the best set of features can be obtained [4]. PSO is firstly introduced by Kennedy and Eberhart [15], is one of the computation technique which is inspired by behavior of flying birds and their means of information exchange to solve the problems.…”

Section: Feature Selection Algorithmsmentioning

confidence: 99%

“…Classifier ensemble or multiple classifier system (MCS) has been widely employed for IDSs since they have better performance in comparison with single classifier [4]. It is deployed by incorporating several base classifiers to predict final class output.…”

Section: Introductionmentioning

confidence: 99%

HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

Tama

Rhee

2017

IEICE Trans. Inf. & Syst.

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SUMMARYAnomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e. particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) for feature selection and ensemble of four tree-based classifiers, i.e. random forest (RF), naive bayes tree (NBT), logistic model trees (LMT), and reduces error pruning tree (REPT) for classification. Proposed approach is implemented on NSL-KDD dataset and from the experimental result, it significantly outperforms the existing methods in terms of accuracy and false alarm rate.

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“…In literature, several approaches for classifiers combination proposed. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], [22,23,24,25,26,27,28,29,30,31] …”

Section: Hybrid and Ensemble Pattern Recognitionmentioning

confidence: 99%

Review of IDS Develepment Methods in Machine Learning

Aburomman

Reaz

2016

IJECE

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Due to the rapid advancement of knowledge and technologies, the problem of decision making is getting more sophisticated to address, therefore the inventing of new methods to solve it is very important. One of the promising directions in machine learning and data mining is classifier combination. The popularity of this approach is confirmed by the still growing number of publications. This review paper focuses mainly on classifier combination known also as combined classifier, multiple classifier systems, or classifier ensemble. Eventually, recommendations and suggestions have also included.

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A Combination of PSO-Based Feature Selection and Tree-Based Classifiers Ensemble for Intrusion Detection Systems

Cited by 48 publications

References 15 publications

Multiple Model Tree Meta Algorithms Improvement of Network Intrusion Detection Predictions Accuracy

Multiple Model Tree Meta Algorithms Improvement of Network Intrusion Detection Predictions Accuracy

HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

Review of IDS Develepment Methods in Machine Learning

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