An intrusion detection algorithm based on bag representation with ensemble support vector machine in cloud computing

Wei, Jinxia; Long, Chun; Li, Jiawei; Zhao, Jing

doi:10.1002/cpe.5922

Cited by 25 publications

(13 citation statements)

References 36 publications

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“…In addition, Raspberry Pi was used to evaluate the response time of classifiers on IoT specific hardware. Wei et al [54] established an intrusion detection algorithm based on ensemble support vector machine with bag representation. Lastly, Zhou et al [105] proposed several steps for their intrusion detection framework.…”

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

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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

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“…The accuracy of 84.2% with the MultiTree algorithm, while the adaptive voting algorithm's ultimate accuracy of 85.2%. Wei et al 20 have designed an intrusion detection method that uses a bag representation and an ensemble support vector machine. The proposed technique identifies the persistent attack with 90.58% recall, according to experimental results on Kyoto 2006+ datasets.…”

Section: Related Workmentioning

confidence: 99%

Network intrusion detection based on ensemble classification and feature selection method for cloud computing

Krishnaveni¹,

Sivamohan²,

Sridhar³

et al. 2022

Concurrency and Computation

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Cloud computing security is the most critical factor for providers, cloud users, and organizations. The various novel approaches apply host‐based or network‐based methods to increase cloud security performance and detection rate. However, due to the virtual and distributed environment of the cloud, conventional network intrusion detection systems (NIDS) have been unreliable in handling these security attacks. Therefore, we design a methodology that incorporates feature selection and classification using ensemble techniques to provide efficient and accurate intrusion detection to address these problems. This proposed model combines the three most effective feature selection techniques (gain‐ratio, chi‐squared, and information gain) to offer a qualifying result and four top classifiers (SVM, LR, NB, and DT) using enhanced weighted majority voting. Moreover, we proposed an experimental technique using a new dataset called Honeypot. All experiments utilized three datasets: Honeypots, Kyoto, and NSL: KDD. In addition, the results of this experimental study were compared with other approaches and performed the statistical significance analysis. Finally, the results reveal that the proposed intrusion detection based on the Honeypot dataset was better and more efficient than other methods because we have an accuracy of 98.29%, FAR of 0.012%, DR of 97.9%, and AUC = 0.9921.

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“…However, the main intention is to detect different possible attacks with lower false alarm rate this means the detection mechanism accurately find the attack or the abnormal behavior. The artificial intelligence 22 and number of machine learning methods is developed in the literature works to detect the attacks and they includes decision tree (DT), support vector machine (SVM), naive Bayes, k‐nearest neighbor (KNN), and so on 23 . Recently, the deep learning methods are developed that combines feature selection and the classification process in a single deep learning model to be used in the IDS 8,24 …”

Section: Introductionmentioning

confidence: 99%

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Sathiyadhas¹,

Vigila

2022

Adaptive Control & Signal

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SummaryIn cloud computing, the resources and memory are dynamically allocated to the user based on their needs. Security is considered as a major issue in cloud as the use of cloud is increased. Intrusion detection is considered as a significant tool to develop a reliable and secure cloud environment. Performing intrusion detection in cloud is a difficult task because of its distributed nature and extensive usage. Intrusion detection system (IDS) is widely considered to find the malicious actions in network. In cloud, most conventional IDS are vulnerable to attacks and have no capability for maintaining the balance between sensitivity and accuracy. Thus, we proposed an effective dragonfly improved invasive weed optimization‐based Shepard convolutional neural network (DIIWO‐based ShCNN) to detect the intruders and to mitigate the attacks in cloud paradigm and are more feasible to detect the intruders with ShCNN. The proposed method outperforms the existing method with maximum accuracy of 0.960%, sensitivity of 0.967%, and specificity 0.961%, respectively.

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An intrusion detection algorithm based on bag representation with ensemble support vector machine in cloud computing

Cited by 25 publications

References 36 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

Network intrusion detection based on ensemble classification and feature selection method for cloud computing

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

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