EBJRV: An Ensemble of Bagging, J48 and Random Committee by Voting for Efficient Classification of Intrusions

Niranjan, A; Prakash, Anusha; Veena, N; Geetha, M.; Shenoy, P. Deepa; Venugopal, K R

doi:10.1109/wiecon-ece.2017.8468876

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…The modulation scheme chosen as the winner is the one with the most votes at that point. It's important to note that Bagging improves identification performance primarily by minimizing variance error (25). The complete process is shown in Figure 3…”

Section: Proposed Modelmentioning

confidence: 99%

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Otoom¹,

Sattar²,

Sadig³

2023

Adv. Sci. Technol. Res. J.

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Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.

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Section: Proposed Modelmentioning

confidence: 99%

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Otoom¹,

Sattar²,

Sadig³

2023

Adv. Sci. Technol. Res. J.

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“…Hence, over-fitting of data is minimized by bagging. Bagging was used for the classification of NSL-KDD test dataset by voting in [6]. The classification process used random tree and the accuracy by bagging was highest among the other methods at the test phase.…”

Section: Machine Learning and Cybersecuritymentioning

confidence: 99%

Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector

Ahsan

Gomes

Chowdhury

et al. 2021

JCP

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Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.

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“…RC has been employed in many fields. It has been combined with ANN for electrical disturbance classification [86], with the random tree by voting for classifying anomalies [87], and with bagging and J48 algorithms for efficient intrusions classification [88]. In this study, RC was used to predict EC.…”

Section: Random Committee (Rc)mentioning

confidence: 99%

River Water Salinity Prediction Using Hybrid Machine Learning Models

Melesse

Khosravi

Tiefenbacher

et al. 2020

Water

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Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.

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EBJRV: An Ensemble of Bagging, J48 and Random Committee by Voting for Efficient Classification of Intrusions

Cited by 12 publications

References 2 publications

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector

River Water Salinity Prediction Using Hybrid Machine Learning Models

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