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Related Experiment Video

Updated: Nov 23, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Published on: October 27, 2023

Artificial immune intelligence-inspired dynamic real-time computer forensics model.

Zairong Wang¹, Xuan Tang², Haohuai Liu³

¹Data Recovery Key Laboratory of Sichuan Province, School of Computer Science, Neijiang Normal University, Sichuan 641100, China.

Mathematical Biosciences and Engineering : MBE

|December 31, 2020

Summary

This summary is machine-generated.

This study introduces a novel dynamic computer forensics model using an artificial immune system to detect and evaluate network risks in real-time. The model enhances efficiency and performance in computer forensics investigations.

Keywords:

artificial immune system computer forensics network security

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Area of Science:

Computer Forensics
Artificial Intelligence
Network Security

Background:

Dynamic computer forensics integrates network intrusion and computer forensics.
Existing models have limitations in real-time efficiency and performance.

Purpose of the Study:

To propose a novel dynamic computer forensics model based on an artificial immune system.
To improve real-time risk evaluation and computer forensics accomplishment.

Main Methods:

Simulating artificial immune mechanisms to define self, non-self, and immunocytes in network transactions.
Detailing evolution processes for immature, mature, and memory detectors.
Constructing real-time network risk evaluation equations.

Main Results:

The proposed model dynamically captures real-time system status (CPU, memory, network bandwidth utilization).
Real-time network risk evaluation equations compute the risk of various network attacks.
Comparative experiments show improved real-time efficiency and performance.

Conclusions:

The artificial immune system-based model offers enhanced dynamic computer forensics capabilities.
The model demonstrates superior real-time efficiency and performance with lower technical demands.