A survey on space-air-ground-sea integrated network security in 6G

H Guo, J Li, J Liu, N Tian, N Kato - … Communications Surveys & …, 2021 - ieeexplore.ieee.org
Space-air-ground-sea integrated network (SAGSIN), which integrates satellite
communication networks, aerial networks, terrestrial networks, and marine communication …

Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges

K Ahmad, M Maabreh, M Ghaly, K Khan, J Qadir… - Computer Science …, 2022 - Elsevier
As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …

Backdoor learning: A survey

Y Li, Y Jiang, Z Li, ST Xia - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

Backdoor attacks and countermeasures on deep learning: A comprehensive review

Y Gao, BG Doan, Z Zhang, S Ma, J Zhang, A Fu… - arXiv preprint arXiv …, 2020 - arxiv.org
This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …

Adversarial machine learning in wireless communications using RF data: A review

D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems

J Liu, M Nogueira, J Fernandes… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

Channel-aware adversarial attacks against deep learning-based wireless signal classifiers

B Kim, YE Sagduyu, K Davaslioglu… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

Generative adversarial network in the air: Deep adversarial learning for wireless signal spoofing

Y Shi, K Davaslioglu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The spoofing attack is critical to bypass physical-layer signal authentication. This paper
presents a deep learning-based spoofing attack to generate synthetic wireless signals that …

Over-the-air adversarial attacks on deep learning based modulation classifier over wireless channels

B Kim, YE Sagduyu, K Davaslioglu… - 2020 54th Annual …, 2020 - ieeexplore.ieee.org
We consider a wireless communication system that consists of a transmitter, a receiver, and
an adversary. The transmitter transmits signals with different modulation types, while the …

Design and evaluation of a multi-domain trojan detection method on deep neural networks

Y Gao, Y Kim, BG Doan, Z Zhang… - … on Dependable and …, 2021 - ieeexplore.ieee.org
Trojan attacks on deep neural networks (DNNs) exploit a backdoor embedded in a DNN
model that can hijack any input with an attacker's chosen signature trigger. Emerging …