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Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

Computers, Materials & Continua 2022年第6期71卷 5117-5133页

作者： S.Sreenivasa Chakravarthi R.Jagadeesh Kannan V.Anantha Natarajan Xiao-Zhi Gao CSE Center for Intelligent ComputingSree Vidyanikethan Engineering College&School of Computer Science and EngineeringVITChennaiTamilnadu600127India School of Computer Science and Engineering VITChennaiTamilnadu600127India CSE Center for Intelligent ComputingSree Vidyanikethan Engineering CollegeTirupati517102India School of Computing University of Eastern FinlandKuopio02130EspooFinland

In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology,and invest in building *** the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering *** this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing *** part of the innovations happening in the industrial sector,a new concept termed as‘smart manufacturing’has emerged,which employs the benefits of emerging technologies like internet of things and cloud *** services deliver an on-demand access to computing,storage,and infrastructural platforms for the industrial users through *** the recent era of information technology the number of business and individual users of cloud services have been increased and larger volumes of data is being processed and stored in *** a consequence,the data breaches in the cloud services are also increasing day by *** to various security vulnerabilities in the cloud architecture;as a result the cloud environment has become *** restore the normal behavior of the cloud,detect the deviations,and achieve higher resilience,anomaly detection becomes *** deep learning architectures-based anomaly detection mechanisms uses various monitoring metrics characterize the normal behavior of cloud services and identify the abnormal *** paper focuses on designing an intelligent deep learning based approach for detecting cloud anomalies in real time to make it more *** deep learning models are trained using features extracted from the system level and network level performance metrics observed in the Transfer Control Protocol(TCP)traces of the *** experimental results of the proposed approach demonstrate a superior performance in terms

关键词： Anomaly detection network flow data deep learning migration auto-encoder support vector machine

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