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Temporal Data Correlation Providing Enhanced Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation.

Abdullah Alqahtani1,2, Frederick T Sheldon2

  • 1College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.

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|May 13, 2023
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Summary
This summary is machine-generated.

This study introduces a new method to detect ransomware attacks early by correlating cryptographic API calls with I/O Request Packets using timestamps. This approach improves detection accuracy, outperforming existing methods for identifying pre-encryption boundaries.

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Application Programming Interface (API)I/O Request Packet (IRP)crypto-ransomwaredata-centricearly detectionevent-based detectionmalwareprocess-centric

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Area of Science:

  • Cybersecurity
  • Malware Analysis
  • Machine Learning

Background:

  • Ransomware uses encryption, making files inaccessible without a key.
  • Early detection models monitor cryptographic API calls.
  • Cryptographic API calls alone are unreliable indicators of ransomware due to legitimate uses.

Purpose of the Study:

  • To propose a novel method for accurately delineating the pre-encryption boundary of ransomware attacks.
  • To overcome the limitations of relying solely on cryptographic API monitoring.

Main Methods:

  • Developed a Temporal Data Correlation method linking cryptographic APIs with I/O Request Packets (IRPs) via timestamps.
  • Extracted features from a pre-encryption dataset for model training.
  • Evaluated performance using various machine and deep learning classifiers.

Main Results:

  • The proposed Temporal Data Correlation method demonstrates higher detection accuracy.
  • The approach effectively delineates the pre-encryption boundary for improved ransomware detection.

Conclusions:

  • Temporal Data Correlation offers a more accurate approach to early ransomware detection.
  • This method enhances the reliability of identifying ransomware threats before encryption occurs.