TIM-8715 v2

Gaba, S., Budhiraja, I., Kumar, V., Martha, S., Khurmi, J., Singh, A., Singh, K. K., Askar, S. S., & Abouhawwash, M. (2024). A systematic analysis of enhancing cyber security using deep learning for cyber physical systems. IEEE Access, Access, IEEE, 12, 6017–6035.
This article systematically analyzes how deep learning techniques can significantly enhance cybersecurity defenses for cyber-physical systems by improving anomaly detection, threat prediction, and overall system resilience.

NU Dissertation Center. (n.d.) Problem statement overview. National University.
This resource provides information on how problem statements are developed to specify and describe a problem.

Munir, N. (2024). The impact of ISO 27001 compliance on employees’ information security awareness and behaviors in medium sized enterprises (MSEs).
This study investigates how adherence to the ISO 27001 standard influences the information security awareness and behavioral practices of employees within medium-sized enterprises.

Liso, A., Cardellicchio, A., Patruno, C., Nitti, M., Ardino, P., Stella, E., & Renò, V. (2024). A review of deep learning-based anomaly detection strategies in industry 4.0 focused on application fields, sensing equipment, and algorithms. In IEEE Access, vol. 12, pp. 93911-93923.
This article reviews deep learning-based anomaly detection strategies applied in Industry 4.0, focusing on their diverse application fields, the types of sensing equipment utilized, and the specific algorithms employed.