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 |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Sefati, Seyed Salar |u National University of Science and Technology POLITEHNICA Bucharest, Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X); Research Center Campus, POLITEHNICA Bucharest, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X); Faculty of Engineering and Natural Science, Istinye University, Department of Software Engineering, Istanbul, Türkiye (GRID:grid.508740.e) (ISNI:0000 0004 5936 1556) | |
| 245 | 1 | |a SSLA: a semi-supervised framework for real-time injection detection and anomaly monitoring in cloud-based web applications with real-world implementation and evaluation | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Datasets | ||
| 653 | |a Data integrity | ||
| 653 | |a User behavior | ||
| 653 | |a Applications programs | ||
| 653 | |a Cloud computing | ||
| 653 | |a Graph representations | ||
| 653 | |a Real time | ||
| 653 | |a Quality of service | ||
| 653 | |a Methods | ||
| 653 | |a Unstructured data | ||
| 653 | |a Semi-supervised learning | ||
| 653 | |a Anomalies | ||
| 653 | |a Privacy | ||
| 653 | |a Monitoring | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Arasteh, Bahman |u Faculty of Engineering and Natural Science, Istinye University, Department of Software Engineering, Istanbul, Türkiye (GRID:grid.508740.e) (ISNI:0000 0004 5936 1556); Khazar University, Department of Computer Science, Baku, Azerbaijan (GRID:grid.442897.4) (ISNI:0000 0001 0743 1899); Applied Science Research Center, Applied Science Private University, Amman, Jordan (GRID:grid.411423.1) (ISNI:0000 0004 0622 534X) | |
| 700 | 1 | |a Fratu, Octavian |u National University of Science and Technology POLITEHNICA Bucharest, Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X); Research Center Campus, POLITEHNICA Bucharest, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X); Academy of Romanian Scientists, Bucharest, Romania (GRID:grid.435118.a) (ISNI:0000 0004 6041 6841) | |
| 700 | 1 | |a Halunga, Simona |u National University of Science and Technology POLITEHNICA Bucharest, Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X); Research Center Campus, POLITEHNICA Bucharest, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X); Academy of Romanian Scientists, Bucharest, Romania (GRID:grid.435118.a) (ISNI:0000 0004 6041 6841) | |
| 773 | 0 | |t Journal of Cloud Computing |g vol. 14, no. 1 (Dec 2025), p. 38 | |
| 786 | 0 | |d ProQuest |t Research Library | |
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