Enhancing Security of Databases through Anomaly Detection in Structured Workloads
In today’s world, the protection of databases in any global organization has become paramount due to the rapid growth of data and the new generations of cyber threats. This highlights the need for more enhanced security precautions to secure these databases containing sensitive information. One of...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
ITB Journal Publisher
2025-02-01
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| Series: | Journal of ICT Research and Applications |
| Subjects: | |
| Online Access: | http://167.205.195.146/ojsnew/index.php/jictra/article/view/23386 |
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| Summary: | In today’s world, the protection of databases in any global organization has become paramount due to the rapid growth of data and the new generations of cyber threats. This highlights the need for more enhanced security precautions to secure these databases containing sensitive information. One of the most advanced ways of enhancing database security is using an anomaly detection system, especially for structured workloads. Structured workloads typically exhibit predictable patterns of data access and usage, making them susceptible to displaying anomalies that may indicate unauthorized access, data manipulation, or other security breaches. Anomaly detection methods can identify patterns that are unusual, an indication of malicious activity, or a data security breach. The present research utilized the Isolation Forest algorithm to detect outliers in high-dimensional data sets. The main contribution and novelty of this research lies in leveraging the Isolation Forest algorithm for structured database workloads to proactively identify and mitigate potential security threats. Our study showed that the proposed model, with an accuracy of 85%, outperformed various state-of-the-art methods. Furthermore, anomaly detection systems powered by advanced algorithms and machine learning enable real-time database activities analysis, addressing challenges like preprocessing, model training and scalability.
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| ISSN: | 2337-5787 2338-5499 |