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SSLA: a semi-supervised framework for real-time injection detection and anomaly monitoring in cloud-based web applications with real-world implementation and evaluation

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Publicado en:Journal of Cloud Computing vol. 14, no. 1 (Dec 2025), p. 38
Autor principal: Sefati, Seyed Salar
Otros Autores: Arasteh, Bahman, Fratu, Octavian, Halunga, Simona
Publicado:
Springer Nature B.V.
Materias:
Machine learning
Datasets
Data integrity
User behavior
Applications programs
Cloud computing
Graph representations
Real time
Quality of service
Methods
Unstructured data
Semi-supervised learning
Anomalies
Privacy
Monitoring
Efficiency
Acceso en línea:Citation/Abstract
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Resumen:Injection attacks and anomalies pose significant threats to the security and reliability of cloud-based web applications. Traditional detection methods, such as rule-based systems and supervised learning techniques, often struggle to adapt to evolving threats and large-scale, unstructured log data. This paper introduces a novel framework, the Semi-Supervised Log Analyzer (SSLA), designed for real-time injection detection and anomaly monitoring in cloud environments. SSLA uses semi-supervised learning to utilize both labeled and unlabeled data, reducing the reliance on extensive annotated datasets. A similarity graph is built from the log data, allowing for effective anomaly detection using graph-based methods. At the same time, privacy-preserving techniques are integrated to protect sensitive information. The proposed method is evaluated on large-scale datasets, including Hadoop Distributed File System (HDFS) and BlueGene/L (BGL) logs, demonstrating superior performance in terms of precision, recall, and scalability compared to state-of-the-art methods. SSLA achieves high detection accuracy with minimal computational overhead, ensuring reliable, real-time protection for cloud-based web applications.
ISSN:2192-113X
DOI:10.1186/s13677-025-00765-6
Fuente:Research Library