Research of machine and deep learning methods application for host-level intrusion detection and classification

Čeponis, Dainius

doi:10.20334/2021-008-m

2021

DOI: 10.20334/2021-008-m

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Research of machine and deep learning methods application for host-level intrusion detection and classification

Dainius Čeponis¹

Abstract: Annex A. The Result Tables of Deep Learning ExperimentsTable A.1. Malware type classification with the help of DL and SVM methods (AllMalware set) Accuracy (percent) Classification time (milliseconds) Count LSTM GRU CNN SVM LSTM GRU CNN SVM 10 88.1

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2024

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Data Storage and Transmission Security in the Cloud

Nayak,

Patnaik,

Satpathy

et al. 2024

Improving Security, Privacy, and Trust in Cloud Computing

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Cloud computing has profoundly changed the face of data management for enterprises, providing increased scalability, ease of access, and cost savings. Nonetheless, this change has highlighted the crucial need for strengthened security measures to protect sensitive data from the ever-changing spectrum of cyber threats. Following the cloud's rise as a storehouse for large datasets, the quest for sophisticated security solutions has gained traction. This motivation has resulted in the incorporation of artificial intelligence (AI) into the cloud security architecture. As cloud storage becomes increasingly popular, organizations are becoming more concerned about data security. Sensitive data is transmitted, ranging from messages and images to financial and health information. As technology advances, there is a growing threat to customer data in the cloud, making greater cloud security more important than ever. This study aims to give a comprehensive insight into the role of AI in data storage and transmission security in the cloud.

show abstract

Data Storage and Transmission Security in the Cloud

Nayak,

Patnaik,

Satpathy

et al. 2024

Improving Security, Privacy, and Trust in Cloud Computing

View full text Add to dashboard Cite

show abstract

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Research of machine and deep learning methods application for host-level intrusion detection and classification

Abstract: Annex A. The Result Tables of Deep Learning ExperimentsTable A.1. Malware type classification with the help of DL and SVM methods (AllMalware set) Accuracy (percent) Classification time (milliseconds) Count LSTM GRU CNN SVM LSTM GRU CNN SVM 10 88.1

Cited by 1 publication

References 164 publications

Data Storage and Transmission Security in the Cloud

Data Storage and Transmission Security in the Cloud

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