JABR, Ismael, SALMAN, Yanal, SHQAIR, Motasem und HAWASH, Amjad, 2025. Penetration Testing and Attack Automation Simulation: Deep Reinforcement Learning Approach. An-Najah University Journal for Research, A: Natural Sciences. 1 Februar 2025. Vol. 39, no. 1, p. 7-14. DOI 10.35552/anujr.a.39.1.2231.
Elsevier - Harvard (with titles)Jabr, I., Salman, Y., Shqair, M., Hawash, A., 2025. Penetration Testing and Attack Automation Simulation: Deep Reinforcement Learning Approach. An-Najah University Journal for Research, A: Natural Sciences 39, 7-14. https://doi.org/10.35552/anujr.a.39.1.2231
American Psychological Association 7th editionJabr, I., Salman, Y., Shqair, M., & Hawash, A. (2025). Penetration Testing and Attack Automation Simulation: Deep Reinforcement Learning Approach. An-Najah University Journal for Research, A: Natural Sciences, 39(1), 7-14. https://doi.org/10.35552/anujr.a.39.1.2231
Springer - Basic (author-date)Jabr I, Salman Y, Shqair M, Hawash A (2025) Penetration Testing and Attack Automation Simulation: Deep Reinforcement Learning Approach.. An-Najah University Journal for Research, A: Natural Sciences 39:7-14. https://doi.org/10.35552/anujr.a.39.1.2231
Juristische Zitierweise (Stüber) (Deutsch)Jabr, Ismael/ Salman, Yanal/ Shqair, Motasem/ Hawash, Amjad, Penetration Testing and Attack Automation Simulation: Deep Reinforcement Learning Approach., An-Najah University Journal for Research, A: Natural Sciences 2025, 7-14.