Abstract
In spite of the tangible advantages of cloud computing, it is still vulnerable to potential attacks and threats. In light of this, security has turned into one of the main concerns in the adoption of cloud computing. Therefore, an anomaly detection method plays an important role in providing a high protection level for network security. One of the challenges in anomaly detection, which has not been seriously considered in the literature, is applying the dynamic nature of cloud traffic in its prediction while maintaining an acceptable level of accuracy besides reducing the computational cost. On the other hand, to overcome the issue of additional training time, introducing a high-speed algorithm is essential. In this paper, a network traffic anomaly detection model grounded in Catastrophe Theory is proposed. This theory is effective in depicting sudden change processes of the network due to the dynamic nature of the cloud. Exponential Moving Average (EMA) is applied for the state variable in sliding window to better show the dynamicity of cloud network traffic. Entropy is used as one of the control variables in catastrophe theory to analyze the distribution of traffic features. Our work is compared with Wei Xiong et al.’s Catastrophe Theory and achieved a maximum improvement in the percentage of Detection Rate in week 4 Wednesday (7.83%) and a 0.31% reduction in False Positive Rate in week 5 Monday. Additional accuracy parameters are checked and the impact of sliding window size in sensitivity and specificity is considered.
Highlights
Nowadays cloud computing is the fastest-growing distributed computational platform in domains such as industries and research communities
The importance of cloud networks has led to attacks on such networks by intruders via malicious attacks which will affect user applications and cloud resources causing a delay in the execution process within the overall cloud computing application [4]
In this part of the section, we propose our anomaly detection method based on the modified Catastrophe Theory and discuss our contributions based on Xiong et al.’s Catastrophe Theory to detect anomalies in cloud network traffic
Summary
Nowadays cloud computing is the fastest-growing distributed computational platform in domains such as industries and research communities. Connected resources through various distributed networks form the cloud [1]. The network is a pivotal part of the cloud which provides quality of service, namely, ensuring the time constraints. Integrations of various computation and storage resources are impossible [2]. It fulfills two important roles in the cloud environment: interacting with user application for connecting to the appropriate resource and sending back the output to the users [3]. The importance of cloud networks has led to attacks on such networks by intruders via malicious attacks which will affect user applications and cloud resources causing a delay in the execution process within the overall cloud computing application [4]
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