Classification of Virtual Attacks using deep learning Techniques
Budget: ₹750 – ₹1,250 INR
Looking for help to complete the Project: (Communication Systems)
In the ref paper shared for local model training they have used a unsupervised deep learning model called AMCNN LSTM , but we are planning to use supervised deep Learning model called Adversarial autoencoder, and the aggregation algorithm for federated learning used here is Fedavg, but we need FedProx and the gradient compression scheme mentioned in the same paper also needs to be implemented along with it .The datasets that needs to be used are 1) NF-TON-IOT dataset 2) NF-BOT-IOT dataset
Reference document and more details on the Project are attached.
In the ref paper shared for local model training they have used a unsupervised deep learning model called AMCNN LSTM , but we are planning to use supervised deep Learning model called Adversarial autoencoder, and the aggregation algorithm for federated learning used here is Fedavg, but we need FedProx and the gradient compression scheme mentioned in the same paper also needs to be implemented along with it .The datasets that needs to be used are 1) NF-TON-IOT dataset 2) NF-BOT-IOT dataset
Reference document and more details on the Project are attached.