A review on intrusion detection approaches in cloud security systems

Singh, Satyapal; Kubendiran, Mohan; Sangaiah, Arun Kumar

doi:10.1504/ijguc.2019.10022139

Cited by 7 publications

(2 citation statements)

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“…Generally, there are the following two methods: (1) Improve the algorithm. Solve the problems caused by sample imbalance from the algorithm level, such as introducing sensitive cost function into the algorithm; (2) Solve from the data level. Tat is, before training the model, adjust the distribution of data samples in the training set.…”

Section: Design Of Data Acquisition Modulementioning

confidence: 99%

“…With the continuous development of network technology, cloud computing has become a pillar technology of Internet applications in various felds [1,2]. However, the universality of cloud computing applications and their virtualization, isomerization, dynamic complexity, and other characteristics also lead to the cloud computing platform becoming the target of many hacker attacks, and the complex structure of the cloud computing platform also makes it face many security threats [3].…”

Section: Introductionmentioning

confidence: 99%

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Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment

Wang

Chen

2023

Journal of Electrical and Computer Engineering

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Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the “gain rate” optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms.

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Section: Design Of Data Acquisition Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment

Wang

Chen

2023

Journal of Electrical and Computer Engineering

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Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Liu

Cheng

et al. 2021

Concurrency and Computation

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Abrupt development of resources and rising expenses of infrastructure are leading institutions to take on cloud computing. Albeit, the cloud environment is vulnerable to various sorts of attacks. So, recognizing malicious software is one of the principal challenges in cloud security governance. Intrusion detection system (IDS) has turned to the most generally utilized element of computer system security that asserts the cloud from diverse sorts of attacks and threats. As evident, no systematic literature review exists that focuses on cloud computing usage within IDS processes. The previous investigations had not considered the statistical analysis method. Hence, this paper examined the IDS mechanisms in cloud computing systematically. Twenty-two articles have been obtained using defined filters divided into four sections: hypervisor-based IDS, network-based IDS, machine learning-based IDS, and hybrid IDS. The comparison is performed depending on the outcomes illustrated in the investigations. It demonstrates that IDS precision, inclusiveness, overhead, and reaction time have been discussed in many studies. Simultaneously, less attention has been paid to cost-sensitivity, functioning, attack tolerance, and intrusion facing. This paper has made an excellent effort to organize literature drawn from multiple sources into a manuscript.

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Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection

et al. 2019

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A review on intrusion detection approaches in cloud security systems

Cited by 7 publications

References 0 publications

Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment

Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment

Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection

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