Explanation-guided backdoor attacks on model-agnostic rf fingerprinting
Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF)
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …
Toward native explainable and robust AI in 6G networks: Current state, challenges and road ahead
Abstract 6G networks are expected to face the daunting task of providing support to a set of
extremely diverse services, each more demanding than those of previous generation …
extremely diverse services, each more demanding than those of previous generation …
Explora: Ai/ml explainability for the open ran
The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a
system of disaggregated, virtualized, and software-based components. These self-optimize …
system of disaggregated, virtualized, and software-based components. These self-optimize …
GRL-PS: Graph embedding-based DRL approach for adaptive path selection
W Wei, L Fu, H Gu, Y Zhang, T Zou… - … on Network and …, 2023 - ieeexplore.ieee.org
Forwarding path selection for data traffic is one of the most fundamental operations in
computer networks, whose performance drastically impacts both transmission efficiency and …
computer networks, whose performance drastically impacts both transmission efficiency and …
Efficient and secure federated learning against backdoor attacks
Due to the powerful representation ability and superior performance of Deep Neural
Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from …
Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from …
Identification of encrypted and malicious network traffic based on one-dimensional convolutional neural network
Y Zhou, H Shi, Y Zhao, W Ding, J Han, H Sun… - Journal of Cloud …, 2023 - Springer
The rapid advancement of the Internet has brought a exponential growth in network traffic. At
present, devices deployed at edge nodes process huge amount of data, extract key features …
present, devices deployed at edge nodes process huge amount of data, extract key features …
Securing distributed network digital twin systems against model poisoning attacks
In the era of 5G and beyond, the increasing complexity of wireless networks necessitates
innovative frameworks for efficient management and deployment. Digital twins (DTs) …
innovative frameworks for efficient management and deployment. Digital twins (DTs) …
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Federated Learning (FL) offers a distributed framework to train a global control model across
multiple base stations without compromising the privacy of their local network data. This …
multiple base stations without compromising the privacy of their local network data. This …
Spotting deep neural network vulnerabilities in mobile traffic forecasting with an explainable AI lens
The ability to forecast mobile traffic patterns is key to resource management for mobile
network operators and planning for local authorities. Several Deep Neural Networks (DNN) …
network operators and planning for local authorities. Several Deep Neural Networks (DNN) …
Explanation-Guided Backdoor Attacks Against Model-Agnostic RF Fingerprinting Systems
Despite the proven capabilities of deep neural networks (DNNs) in identifying devices
through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep …
through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep …