FCM–SVM based intrusion detection system for cloud computing environment

Jaber, Aws Naser; Rehman, Shafiq Ul

doi:10.1007/s10586-020-03082-6

Cited by 82 publications

(20 citation statements)

References 26 publications

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“…Finally, the fuzzy aggregation model combined the results of the hypervisor inspector. 18 Edge computing expands conventional services of cloud to the network edge, and the exceptionally dynamic and heterogeneous condition at the network edge creates the security of network circumstance confronting extreme difficulties. Yin et al analyzed the improved k-dependency Bayesian network (KDBN) technique that defined the trust relations between system elements and decreased the difficulty of the BN structure by lessening the coordinated weak dependence edges.…”

Section: Related Workmentioning

confidence: 99%

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Manimurugan

Manimegalai

Valsalan

et al. 2020

Int J Communication

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The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, an IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) are used for attack detection and classification. The Canadian Institute for Cybersecurity CIC-IDS 2017 dataset is used for the evaluation of performance analysis.Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model performs better in every performance metric, like accuracy, precision, recall, and detection rate.Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

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Section: Related Workmentioning

confidence: 99%

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Manimurugan

Manimegalai

Valsalan

et al. 2020

Int J Communication

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show abstract

“…Jaber et al 15 proposed a novel intrusion detection system that combines a fuzzy C means clustering (FCM) algorithm with support vector machine (SVM) to improve the accuracy of the detection system in cloud computing environment. The NSLKDD dataset was used for experiments.…”

Section: Previous Workmentioning

confidence: 99%

Intrusion detection system based firefly algorithm‐random forest for cloud computing

Moharamkhani

Hendi

Bandar

et al. 2022

Concurrency and Computation

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Cloud computing is an active environment for accessing shared resources. Security in this environment, is one of the most important issues and concerns. An intrusion detection system is used to provide security and privacy. The intrusion detection system distinguishes between attacks and normal activities and prevents attacks on the cloud environment and notifies managers and cloud providers by creating alerts. Intrusion detection systems are usually implemented with different solutions. In this article, we present an intrusion detection system based on firefly algorithm and random forest algorithm that has the ability to detect intrusion with high accuracy in cloud computing, the results of model testing and evaluation, efficient and appropriate indicates the existence of the proposed method.

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“…The autoencoder helps to generate the decision with partial information of IDs without receiving complete feedback from IDs. [20] reported that accuracy in IDS is a challenging task. To overcome this issue, the authors developed a novel scheme by combining Fuzzy C Means clustering (FCM) and Support Vector Machine (SVM).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Deep Learning Techniques in Cloud Computing to Detect Malicious Network Traffic: A Sustainable Computing Approach

H¹,

Kumar²,

Shenoy³

et al. 2021

IJWMT

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FCM–SVM based intrusion detection system for cloud computing environment

Cited by 82 publications

References 26 publications

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Retracted: Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

Intrusion detection system based firefly algorithm‐random forest for cloud computing

Exploring Deep Learning Techniques in Cloud Computing to Detect Malicious Network Traffic: A Sustainable Computing Approach

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