The rapid evolution of 5G technology, while offering substantial benefits, concurrently presents complex cybersecurity challenges. Current cybersecurity systems often fall short in addressing challenges such as the lack of realism of the 5G network, the limited scope of attack scenarios, the absence of countermeasures, the lack of reproducible, and open-sourced cybersecurity training environments. Addressing these challenges necessitates innovative cybersecurity training systems, referred to as “cyber ranges”. In response to filling these gaps, we propose the Cyber5Gym, an integrated cyber range that enhances the automation of virtualized cybersecurity training in 5G networks with cloud-based deployment. Our framework leverages open-source tools (i) Open5GS and UERANSIM for realistic emulation of 5G networks, (ii) Docker for efficient virtualization of the training infrastructure, (iii) 5Greply for emulating attack scenarios, and (iv) Shell scripts for automating complex training operations. This integration facilitates a dynamic learning environment where cybersecurity professionals can engage in real-time attack and countermeasure exercises, thus significantly improving their readiness against 5G-specific cyber threats. We evaluated it by deploying our framework on Naver Cloud with 20 trainees, each accessing an emulated 5G network and managing 100 user equipments (UEs), emulating three distinct attack scenarios (SMC-Reply, DoS, and DDoS attacks), and exercising countermeasures, to demonstrate the cybersecurity training. We assessed the effectiveness of our framework through specific metrics such as successfully establishing the 5G network for all trainees, accurate execution of attack scenarios, and their countermeasure implementation via centralized control of the master using automated shell scripts. The open-source foundation of our framework ensures replicability and adaptability, addressing a critical gap in current cybersecurity training methodologies and contributing significantly to the resilience and security of 5G infrastructures.

Several reports from prominent national centers monitoring wildfires and emergencies indicate that the impact of wildfire devastation increased by 2.96 folds compared to a decade ago. Various deep-learning solutions have been proposed to tackle this problem, yet there is a lack of interpretability in their classification. To address these shortcomings, we present FireXplainer, an efficient model that leverages transfer learning, fine-tuning techniques, and the Learning without Forgetting (LwF) methodology. This model incorporates convolution blocks and image pre-processing techniques to enhance classification precision. Additionally, we utilize multiple datasets (Kaggle & Mendeley) and apply Explainable AI (XAI)(Grad-CAM) methodology for result interpretation. Our experimental results demonstrate that FireXplainer outperforms state-of-the-art methods and is well-suited for wildfire interpretable image classification.