SXAD: Shapely eXplainable AI-Based Anomaly Detection Using Log Data-Reference-Cited by-同舟云学术

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SXAD: Shapely eXplainable AI-Based Anomaly Detection Using Log Data

Published:2024 Issue: Volume:12 Page:95659-95672
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alam Kashif¹^ORCID,Kifayat Kashif²,Sampedro Gabriel Avelino³^ORCID,Karovič Vincent⁴^ORCID,Naeem Tariq¹

Affiliation:

1. Department of Computer Science, Faculty of Computing and AI, Air University, Islamabad, Pakistan

2. Department of Cyber Security, Faculty of Computing and AI, Air University, Islamabad, Pakistan

3. School of Management and Information Technology, De La Salle-College of Saint Benilde, Manila, Philippines

4. Department of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava, Bratislava, Slovakia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10589622.pdf?arnumber=10589622

Reference53 articles.

1. Anomaly Detection from Log Files Using Data Mining Techniques

2. Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

3. Deep learning for anomaly detection in log data: A survey

4. On Vulnerability and Security Log analysis

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