Research Briefs on Information and Communication Technology Evolution

Evaluation of an Integrated Disaster Response Support System Utilizing Information and Communication Technology

2026-02-07T17:12:37+00:00

In this study, we developed an integrated disaster response support system to improve the efficiency of disaster response operations and integrate information management in local governments. In response to recent issues, e.g., a shortage of specialized staff and difficulties with knowledge transfer, the proposed system was designed to integrate functions, support disaster operations using the latest technologies, and eliminate the dependency on personal knowledge. The results of an effectiveness evaluation by local government employees demonstrated the system’s potential to eliminate information fragmentation and support decision-making processes. In addition, a usability evaluation using the system usability scale rated it highly, confirming that even inexperienced staff can operate the proposed system intuitively. Furthermore, a chatbot equipped with retrieval-augmented generation functionality demonstrated high accuracy rates and efficiency in several information search tasks. The findings of this study indicate that the proposed system combines ease of use with advanced technology, thereby making it a useful platform that will contribute to improving the disaster response capabilities of local governments.

SGX-Enabled Encrypted Storage for Secure Management of 5G Authentication Data in Trusted Execution Environments

2026-02-07T17:20:28+00:00

The fifth-generation (5G) core network, sensitive authentication data such as the Subscription Permanent Identifier (SUPI) and long-term cryptographic keys are centrally stored in the Unified Data Repository (UDR) to support primary authentication. Recent real-world incidents, including the 2025 SK Telecom (SKT) breach, demonstrate that compromise of core servers or databases can expose plaintext subscriber data even when transport-layer security is correctly deployed. This high- lights the need for strong data-at-rest protection and robust cryptographic key isolation within the 5G core. In this paper, we propose a storage-centric protection scheme that preserves the confidentiality and integrity of 5G authentication data even under authentication server compromise. Authentication records are encrypted before being stored in the UDR, with encryption and decryption operations in- voked through database triggers and executed inside an Intel Software Guard Extensions (SGX)-based Trusted Execution Environment (TEE). All cryptographic keys and sensitive operations are fully iso- lated within the enclave, preventing direct access from both the database and application layers. We implement the proposed design on the OpenAirInterface (OAI) 5G core using a MySQL-backed UDR, demonstrating its applicability to real-world and open-source 5G deployments. Performance evalua- tion over 10,000 end to end authentication procedures shows that the proposed approach introduces moderate CPU overhead, particularly during decryption-intensive operations, while incurring negli- gible memory overhead and minimal latency impact. These results indicate that SGX-based storage centric protection is a practical and effective mechanism for strengthening data at rest security in 5G core networks.

Benchmarking Deep Learning Architectures for Real-Time Intrusion Detection in Kubernetes-Orchestrated 5G Core Networks

2026-02-07T17:22:56+00:00

Cloud-native 5G core networks on Service-Based Architecture expose distributed Network Functions to cyber threats requiring adaptive Deep Learning-based Intrusion Detection Systems (DL-IDS). This work evaluates six DL architectures (Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoencoder (AE)) on a Kubernetes-orchestrated Open5GS testbed, measuring Central Processing Unit (CPU) utilization, memory consumption, and latency under realistic traffic conditions. Results show feedforward models (CNN, MLP, AE) achieve sub-millisecond latency (0.6 milliseconds (ms)) with CPU below 12%, enabling multiple concurrent IDS instances per server, while recurrent architectures (RNN, LSTM, GRU) require high CPU utilization (99-107%) with 3.5 to 7.2 ms latency, necessitating dedicated hardware acceleration. Memory footprint remains consistent (385 to 390 megabytes (MB)) across all models. These findings demonstrate that operational efficiency is a key consideration for DL-IDS deployment in production 5G networks, with substantial CPU efficiency differences between architecture choices.