Enhancing Database Security through Machine Learning: Anomaly Detection and Response

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Enhancing Database Security through Machine Learning: Anomaly Detection and Response

Prince Boateng

Instructor:

American Military University

Class:ISSC290

6/16/2024

More sophisticated security measures must be in place as the nature of cyber threats continues to change in favor of complexity, particularly for highly valuable database systems. Efforts to avert new or even complex hazards are hardly supported by basic precautionary measures. Given these conditions, this research article suggests utilizing machine learning (ML) to enhance security by means of anomaly identification and automated reactions to such anomalies within a database system. To detect patterns and indicators of a security breach, large amounts of data can be analyzed using models that incorporate machine learning. The discussion that follows offers many MLALGs in this regard. The benefits of real-time anomaly detection are accessed in order to evaluate real-time threat handling with minimal impact on the database.

In 2020, Gupta and colleagues introduced taxonomy of machine learning models utilized in safe data analytics, highlighting their suitability and constraints for threat discovery and mitigation. The authors highlight the necessity of creating a suitable threat model that will show how cyber threats are always changing and taking on new forms, which is why they advocate for ML-based security solutions that are intelligent and adaptive. Similarly, when employing machine learning models, Xue et al. (2020) talked about security issues and potential solutions. The authors emphasize that evaluating the security of ML-models and countermeasures against hostile attacks is a crucial area of focus for their research. Every one of these examples highlights the potential and needs for machine learning (ML)-based techniques in database security that aim to increase efficiency and capability.

The efficiency of ML-based IDS on imbalanced datasets is the subject of an article by Karatas et al. (2020), which is followed by this discussion. According to this paper, optimized machine learning models will aid in danger identification, lower false alarms, and preserve accessibility and dependability in database systems.

High-level Outline

i.d

Issues in implementing security some databases

Challenges associated with conventional approaches to security.

Essential Machine Learning Concepts and Choices for Anomaly Detection

Objectives and Significance of the Study

ii. Literature Review

A Brief Insight into Machine Learning in Cyber Security,Gupta et al. (2020)

Security Threats and Risk Mitigation Algorithms in Machine Learning (Xue et al., 2020)

The Effectiveness of IDS ML in the Creation and Training of Data Sets,Karatas et al., (2020)

iii. Methodology

Capture and pre-process end-user data

Artificial and live database transaction data.

Machine learning's farthest limit: coming out ahead

Supervised, unsupervised, and semi-sup

Training and Assessment Measures

Accuracy, detection speed, false positive rate

iv. Automated Response Strategies

Design of Automated Response Mechanism

access control, intrusion detection and alarm/notification

Integration with anomaly detection models

Evaluation of response effectiveness

v. Performance Impact Analysis

Securing database performance assessment by machine-learning-based security approaches

Trade-offs between security and performance

Examples or case studies of lived experiences

vi. Discussion

Implications of the Findings

Compare to a regular protocol for security

Challenges and limitations

vi. Conclusion

Summary of key achievements

Future research avidness

Concluding remarks

References

Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2020). Machine learning models for secure data analytics: A taxonomy and threat model.  Computer Communications,  153, 406-440. https://doi.org/10.1016/j.comcom.2020.02.008

Karatas, G., Demir, O., &Sahingoz, O. K. (2020). Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset.  IEEE access,  8, 32150-32162. https://doi.org/10.1109/ACCESS.2020.2973219

Xue, M., Yuan, C., Wu, H., Zhang, Y., & Liu, W. (2020). Machine learning security: Threats, countermeasures, and evaluations.  IEEE Access,  8, 74720-74742. https://doi.org/10.1109/ACCESS.2020.2987435

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