Details

Autor(en) / Beteiligte

• Zhang, Xiaolu
• Cui, Lei
• Shen, Wuqiang
• Zeng, Jijun
• Du, Li
• He, Haoyang
• Cheng, Long

Titel

File processing security detection in multi-cloud environments: a process mining approach

Ist Teil von

• Journal of cloud computing : advances, systems and applications, 2023-12, Vol.12 (1), p.100-10, Article 100

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2023

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• Cloud computing has gained popularity in recent years, but with its rise comes concerns about data security. Unauthorized access and attacks on cloud-based data, applications, and infrastructure are major challenges that must be addressed. While machine learning algorithms have improved intrusion detection systems in cloud data security, they often fail to consider the entire life cycle of file processing, making it difficult to detect certain issues, especially insider attacks. To address these limitations, this paper proposes a novel approach to analyzing data file processing in multi-cloud environments using process mining. By generating a complete file processing event log from a multi-cloud environment, the proposed approach enables detection from both control flow and performance perspectives, providing a deeper understanding of the underlying file processing in its full life cycle. Through our case study, we demonstrate the power and capabilities of process mining for file security detection and showcase its ability to provide further insights into file security in multi-cloud environments.