Scientific Research Publishing

Kea, K., Han, Y. and Kim, T.-K. (2023) Enhancing Anomaly Detection in Distributed Power Systems Using Autoencoder-Based Federated Learning. PLOS ONE, 18, e0290337.

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...
IEEE

Attention-Based LSTM Autoencoder for Noise-Resilient Semantic Communication for IoT Devices in 6G Networks

Abstract: Semantic communication (SC) has emerged as a key enabler for 6G networks to overcome the Shannon limit. SC aims to transmit meaning rather than raw data. In this context, many researchers ...
GitHub

ranasaurabh191/Predictive-Maintenance-PdM-Pipeline

textpdmp_project/ │ ├── data/ │ ├── raw/ # Raw synthetic sensor data (e.g., synthetic_sensor_data.csv) │ └── processed/ # Processed data (e.g ...
Scientific Research Publishing

Alrawais, A., Alhothaily, A., Hu, C. and Cheng, X. (2021) LSTM-Based Intrusion Detection for IoT Networks. IEEE Transactions on Network and Service Management, 18, 1712-1725.

ABSTRACT: The rapid proliferation of the Internet of Things (IoT) and Industrial IoT (IIoT) has revolutionized industries through enhanced connectivity and automation. However, this expansion has ...
GitHub

Aikhusy/Progress-LSTM-AUTOENCODER

fw_load_avg_1_min fw_load_avg_5_min fw_load_avg_15_min fw_cpu_used mem_used root_used log_used fw_total_alloc model saya menemukan bahwa [31, 34, 47, 48, 49, 50, 51 ...