Exploring Time and Frequency Domains for Accurate and Automated Anomaly Detection in Cloud Computing Systems

Guan, Qiang; Fu, Song; DeBardeleben, Nathan; Blanchard, Sean

doi:10.1109/prdc.2013.40

“…In Guan et al (2013), multiscale anomaly identification method based on wavelet is proposed. The method analyses cloud performance metrics by time and frequency of occurrence and detects anomalous cloud behaviour.…”

Section: Related Workmentioning

confidence: 99%

“…Correlation of data indicating the one state of anomaly differs from the correlation of another (Guan et al, 2013). In addition, among the given classifier set the identification accuracy of the various anomalies states of each classifier differs from each other (Əliquliyev et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Hybridisation of classifiers for anomaly detection in big data

Alguliyev

¹

,

Alıguliyev

²

,

Abdullayeva

³

2019

IJBDI

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Recently, the widespread use of cloud technologies has led to the rapid increase in the scale and complexity of this infrastructure. The degradation and downtimes in the performance metrics of these large-scale systems are considered to be a major problem. The key issue in addressing these problems is to detect anomalies that can occur in hardware, software and state of the systems of cloud infrastructure. In this paper, for the detection of anomalies in performance metrics of cloud infrastructure, a semi-supervised classification method based on an ensemble of classifiers is proposed. In the proposed method, to build ensemble Naive Bayes, J48, SMO, multilayer perceptron, IBK and PART algorithms are used. To detect anomalous behaviour on the performance metrics the public data of the Google and Yahoo! companies, Python 2.7, MATLAB, Weka and Google Cloud SDK Shell applications are used. In the result of the experimental study of the model, 90% detection accuracy is obtained.

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“…The work in [24] provided a novel prototype that enabled an online spatio-temporal anomaly detection scheme in a cloud scenario. Thus, the authors were able to initially formulate and further implement a wavelet-based multi-scale anomaly detection system.…”

Section: Anomaly Detection In Cloudsmentioning

confidence: 99%

Malware Detection in Cloud Computing Infrastructures

2018

IJRTER

0

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Abstract-Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.Index Terms-Security, resilience, invasive software, multi-agent systems, network-level security and protection.

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“…A number of anomaly detection techniques [21], [22], [23], [24], [25], [26] aim to proactively and reactively detect cloud-specific threats, but due to their complex statistical measures they mostly lack scalability and often require prior knowledge, thus making them unsuitable for online detection in cloud infrastructures.…”

Section: Anomaly Detection In Cloudsmentioning

confidence: 99%

Malware Detection in Cloud Computing Infrastructures

Watson

¹

,

Shirazi

²

,

Marnerides

³

et al. 2016

IEEE Trans. Dependable and Secure Comput.

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Abstract-Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.Index Terms-Security, resilience, invasive software, multi-agent systems, network-level security and protection.

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Exploring Time and Frequency Domains for Accurate and Automated Anomaly Detection in Cloud Computing Systems

Cited by 17 publications

References 51 publications

Hybridisation of classifiers for anomaly detection in big data

Hybridisation of classifiers for anomaly detection in big data

Malware Detection in Cloud Computing Infrastructures

Malware Detection in Cloud Computing Infrastructures

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