Research article

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

  • Received: 02 July 2020 Accepted: 12 October 2020 Published: 22 October 2020
  • Dynamic computer forensics is a popular area in computer forensics that combines network intrusion technology with computer forensics technology. A novel dynamic computer forensics model is proposed based on an artificial immune system. Simulating the artificial immune mechanism, the definitions of self, non-self, and immunocyte in the network transactions are given. Then, detailed evolution processes for immature detectors, mature detectors, and memory detectors are given. Real-time network risk evaluation equations are constructed, which can compute the risk of each type of network attack. Finally, computer forensics is accomplished according to the real-time network risk. The immune cells dynamically capture the real-time computer system status of the invading antigen, including CPU utilization, memory utilization, network bandwidth utilization status, etc. Theoretical analysis and comparative experimental results demonstrate that the proposed model improves the real-time efficiency and performance with low technical requirements for technicians compared with existing models.

    Citation: Zairong Wang, Xuan Tang, Haohuai Liu, Lingxi Peng. Artificial immune intelligence-inspired dynamic real-time computer forensics model[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7221-7233. doi: 10.3934/mbe.2020370

    Related Papers:

  • Dynamic computer forensics is a popular area in computer forensics that combines network intrusion technology with computer forensics technology. A novel dynamic computer forensics model is proposed based on an artificial immune system. Simulating the artificial immune mechanism, the definitions of self, non-self, and immunocyte in the network transactions are given. Then, detailed evolution processes for immature detectors, mature detectors, and memory detectors are given. Real-time network risk evaluation equations are constructed, which can compute the risk of each type of network attack. Finally, computer forensics is accomplished according to the real-time network risk. The immune cells dynamically capture the real-time computer system status of the invading antigen, including CPU utilization, memory utilization, network bandwidth utilization status, etc. Theoretical analysis and comparative experimental results demonstrate that the proposed model improves the real-time efficiency and performance with low technical requirements for technicians compared with existing models.


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