Weighted vote algorithm combination technique for anomaly based Smart Grid Intrusion Detection systems

Lueckenga, Joris; Engel, Dominik; Green, Robert C.

doi:10.1109/ijcnn.2016.7727543

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Ludwig [75] designed the IDS using a neural network ensemble method, which is based on autoencoder, deep belief neural network, deep neural network, and an extreme learning machine, in order to classify the different network attacks. Lueckenga et al [69] proposed a weighted vote classification method and a general weight calculation function for improving the detection performance of anomaly-based smart grid IDSs. Aburomman and Reaz [38] implemented an ensemble of LDA and PCA for developing an efficient IDS.…”

Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

107

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Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs.

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Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

107

View full text Add to dashboard Cite

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“…This system also detects and classifies different types of network attacks so as to warn them of possible network attacks in advance. Lueckenga et al [19] proposed a generic weight calculation function as well as a generic weight calculation function in order to improve the detection performance of anomaly intrusion detection system. The proposed function solves the problem of poor intrusion detection in smart grid detection systems.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model consists of one input layer, two convolutional layers, two pooling layers and three fully connected layers and one output layer. Finally, a framework that can fully consider the user's privacy protection is proposed. The framework can only exchange model parameters or intermediate results without exchanging local individuals or sample data. This paper further builds a global model based on virtual fusion data, so as to achieve a balance between data privacy protection and data sharing computing. The performance indicators such as accuracy, precision, recall, F1 score, and AUC of the model are verified by simulation. The results show that the model is helpful in solving the problem that the IoT intrusion detection system cannot achieve high precision and low cost at the same time.

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“…In the current work, weighted majority voting was used to classify the data where PSO was employed for assigning weights to various classifiers. Moreover, prior studies on IDS [38,39]…”

Section: Majority Votingmentioning

confidence: 99%

An ensemble based approach for effective intrusion detection using majority voting

2021

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Of late, Network Security Research is taking center stage given the vulnerability of computing ecosystem with networking systems increasingly falling to hackers. On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks. A designated set of reliable safety has been put in place to check any severe damage to the network and the user base. Machine learning (ML) is being frequently used to detect intrusion owing to their understanding of intrusion detection systems in minimizing security threats. However, several single classifiers have their limitation and pose challenges to the development of effective IDS. In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model was proposed which can be employed in real-time for successfully scrutinizing the network traffic to proactively warn about the possibility of attacks. By taking into consideration the properties of existing machine learning algorithms, an effective model was developed and accordingly, an accuracy of 99%, 97.2%, 97.2%, and 93.2% were obtained for DoS, Probe, R2L, and U2R attacks and thus, the proposed model is effective for identifying intrusion.

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Weighted vote algorithm combination technique for anomaly based Smart Grid Intrusion Detection systems

Cited by 6 publications

References 12 publications

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

An ensemble based approach for effective intrusion detection using majority voting

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