Researchers from University of Petra Describe Findings in Artificial Intelligence (A Novel Quantum Epigenetic Algorithm for Adaptive Cybersecurity Threat Detection)

Strategic Insights -

Researchers at the University of Petra have developed a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The QEA was evaluated across four benchmark datasets and consistently outperformed baseline methods, achieving the highest classification accuracy and lowest false positive rates. This breakthrough has significant implications for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments.

Key Takeaways:

  • The Quantum Epigenetic Algorithm (QEA) is a novel optimization framework that combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection.
  • The QEA was evaluated across four benchmark datasets (UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT) and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA).
  • The QEA achieved the highest classification accuracy of up to 97.12% and the lowest false positive rates as low as 1.68% on the TON_IoT dataset.
  • The QEA selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency.
  • The research demonstrated the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments.
  • The QEA has significant implications for improving cybersecurity systems and reducing the risk of cyber threats.

Statistics:

  • The QEA achieved a classification accuracy of up to 97.12% on the UNSW-NB15 dataset.
  • The QEA achieved a false positive rate of up to 1.68% on the TON_IoT dataset.
  • The QEA selected an average of 18 features on the TON_IoT dataset.
  • The QEA maintained near real-time latency on all four benchmark datasets.
  • The research evaluated the QEA across four benchmark datasets: UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT.

Sources:

  • A Novel Quantum Epigenetic Algorithm for Adaptive Cybersecurity Threat Detection. AI, 2025,6(8):165. (https://doi.org/10.3390/ai6080165)
  • NewsRx. Researchers from University of Petra Describe Findings in Artificial Intelligence (A Novel Quantum Epigenetic Algorithm for Adaptive Cybersecurity Threat Detection). Life Science Weekly. September 9, 2025; p 4751.