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SentinelFusion based machine learning comprehensive approach for enhanced computer forensics.

Umar Islam¹, Abeer Abdullah Alsadhan², Hathal Salamah Alwageed³

¹Computer Science, IQRA National University, Peshawar, Swat Campus, Pakistan.

Peerj. Computer Science

|August 15, 2024

Summary

SentinelFusion enhances computer forensics by integrating blockchain security with machine learning. This novel framework significantly improves the detection and prevention of data tampering and security breaches.

Keywords:

Artificial intelligence Computer Security Computer forensics Forensics Machine learning

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Area of Science:

Computer Forensics
Blockchain Technology
Machine Learning

Background:

Modern technology rapidly evolves, creating new challenges for data security and integrity.
Blockchain offers enhanced security features, while machine learning provides advanced analytical capabilities.
Integrating these technologies presents opportunities to improve digital forensic investigations.

Purpose of the Study:

To introduce SentinelFusion, an ensemble machine learning framework for blockchain systems.
To enhance data integrity, privacy, and secrecy within blockchain environments.
To improve the detection and prevention of security breaches and data tampering.

Main Methods:

Developed SentinelFusion, an ensemble-based machine learning framework.
Utilized a comprehensive blockchain dataset of criminal activities.
Employed various machine learning models including SVM, KNN, Naive Bayes, Logistic Regression, Decision Trees, and the SentinelFusion ensemble model.

Main Results:

SentinelFusion demonstrated superior performance compared to individual machine learning models.
Achieved high evaluation metrics: 0.99 accuracy, precision, recall, and F1 score.
Validated the framework's effectiveness in detecting and preventing blockchain-related security threats.

Conclusions:

The convergence of blockchain and machine learning significantly advances computer forensics.
SentinelFusion offers a robust solution for bolstering security and data integrity in blockchain systems.
Findings provide valuable insights for computer forensics practitioners and researchers.