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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| Format: | Article |
| Language: | English |
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ITB Journal Publisher
2025-02-01
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| Series: | Journal of ICT Research and Applications |
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| Online Access: | http://167.205.195.146/ojsnew/index.php/jictra/article/view/23386 |
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| author | Charanjeet Dadiyala Faijan Qureshi Kritika Anil Bhattad Sourabh Thakur Nida Tabassum Sharif Sheikh Kushagra Anil Kumar Singh |
| author_facet | Charanjeet Dadiyala Faijan Qureshi Kritika Anil Bhattad Sourabh Thakur Nida Tabassum Sharif Sheikh Kushagra Anil Kumar Singh |
| author_sort | Charanjeet Dadiyala |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-df98b54496ef40cdaa412d361777aa85 |
| institution | DOAJ |
| issn | 2337-5787 2338-5499 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | ITB Journal Publisher |
| record_format | Article |
| series | Journal of ICT Research and Applications |
| spelling | doaj-art-df98b54496ef40cdaa412d361777aa852025-08-20T02:48:22ZengITB Journal PublisherJournal of ICT Research and Applications2337-57872338-54992025-02-0118310.5614/itbj.ict.res.appl.2025.18.3.2Enhancing Security of Databases through Anomaly Detection in Structured Workloads Charanjeet Dadiyala0Faijan Qureshi1Kritika Anil Bhattad2Sourabh Thakur3Nida Tabassum Sharif Sheikh 4Kushagra Anil Kumar Singh5Shri Ramdeobaba College of Engineering and Management Nagpur, 440013, Shri Ramdeobaba College of Engineering and Management Nagpur, 440013, Shri Ramdeobaba College of Engineering and Management Nagpur, 440013, Shri Ramdeobaba College of Engineering and Management Nagpur, 440013, Shri Ramdeobaba College of Engineering and Management Nagpur, 440013, Shri Ramdeobaba College of Engineering and Management Nagpur, 440013, 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. http://167.205.195.146/ojsnew/index.php/jictra/article/view/23386anomaly detectiondatabase securityIsolation Forestmachine learningMySQLstructured workloads |
| spellingShingle | Charanjeet Dadiyala Faijan Qureshi Kritika Anil Bhattad Sourabh Thakur Nida Tabassum Sharif Sheikh Kushagra Anil Kumar Singh Enhancing Security of Databases through Anomaly Detection in Structured Workloads Journal of ICT Research and Applications anomaly detection database security Isolation Forest machine learning MySQL structured workloads |
| title | Enhancing Security of Databases through Anomaly Detection in Structured Workloads |
| title_full | Enhancing Security of Databases through Anomaly Detection in Structured Workloads |
| title_fullStr | Enhancing Security of Databases through Anomaly Detection in Structured Workloads |
| title_full_unstemmed | Enhancing Security of Databases through Anomaly Detection in Structured Workloads |
| title_short | Enhancing Security of Databases through Anomaly Detection in Structured Workloads |
| title_sort | enhancing security of databases through anomaly detection in structured workloads |
| topic | anomaly detection database security Isolation Forest machine learning MySQL structured workloads |
| url | http://167.205.195.146/ojsnew/index.php/jictra/article/view/23386 |
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