Federated learning for intrusion detection system: Concepts, challenges and future directions

S Agrawal, S Sarkar, O Aouedi, G Yenduri… - Computer …, 2022 - Elsevier
The rapid development of the Internet and smart devices trigger surge in network traffic
making its infrastructure more complex and heterogeneous. The predominated usage of …

Wild patterns reloaded: A survey of machine learning security against training data poisoning

AE Cinà, K Grosse, A Demontis, S Vascon… - ACM Computing …, 2023 - dl.acm.org
The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Invisible backdoor attack with sample-specific triggers

Y Li, Y Li, B Wu, L Li, R He… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Recently, backdoor attacks pose a new security threat to the training process of deep neural
networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the …

Lira: Learnable, imperceptible and robust backdoor attacks

K Doan, Y Lao, W Zhao, P Li - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Recently, machine learning models have demonstrated to be vulnerable to backdoor
attacks, primarily due to the lack of transparency in black-box models such as deep neural …

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 …

Backdoorbench: A comprehensive benchmark of backdoor learning

B Wu, H Chen, M Zhang, Z Zhu, S Wei… - Advances in …, 2022 - proceedings.neurips.cc
Backdoor learning is an emerging and vital topic for studying deep neural networks'
vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being …

Narcissus: A practical clean-label backdoor attack with limited information

Y Zeng, M Pan, HA Just, L Lyu, M Qiu… - Proceedings of the 2023 …, 2023 - dl.acm.org
Backdoor attacks introduce manipulated data into a machine learning model's training set,
causing the model to misclassify inputs with a trigger during testing to achieve a desired …

Backdoor defense via decoupling the training process

K Huang, Y Li, B Wu, Z Qin, K Ren - arXiv preprint arXiv:2202.03423, 2022 - arxiv.org
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor
attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few …

Label poisoning is all you need

R Jha, J Hayase, S Oh - Advances in Neural Information …, 2023 - proceedings.neurips.cc
In a backdoor attack, an adversary injects corrupted data into a model's training dataset in
order to gain control over its predictions on images with a specific attacker-defined trigger. A …

Privacy and security issues in deep learning: A survey

X Liu, L Xie, Y Wang, J Zou, J Xiong, Z Ying… - IEEE …, 2020 - ieeexplore.ieee.org
Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …