D-BADGE: Decision-Based Adversarial Batch Attack With Directional Gradient Estimation by Geunhyeok Yu, Minwoo Jeon, Hyoseok Hwang

“…The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. …”
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Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT by Yonas Kibret Beshah, Surafel Lemma Abebe, Henock Mulugeta Melaku

“…The existing defence technique primarily focuses on batch learning- based DDoS attack detection systems, that are not equipped to handle multiple and unknown adversarial attacks in real time. …”
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Classification and Identification of Frequency-Hopping Signals Based on Jacobi Salient Map for Adversarial Sample Attack Approach by Yanhan Zhu, Yong Li, Tianyi Wei

“…Experimental results in a white-box environment show that, compared with the traditional JSMA method, BPNT-JSMA not only maintains a high attack success rate but also enhances attack efficiency and improves the stealthiness of the adversarial samples.…”
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Defending CNN Against FGSM Attacks Using Beta-Based Personalized Activation Functions and Adversarial Training by Hanen Issaoui, Asma Eladel, Ahmed Zouinkhi, Mourad Zaied, Lazhar Khriji, Sarvar Hussain Nengroo

“…This study proposes a defense strategy to improve the insurance of white-box models while minimizing adversarial attacks against Fast Gradient Sign Method (FGSM)-based attacks and tackling the issue of adversarial training to improve their robustness. …”
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Block Encryption LAyer (BELA): Zero-Trust Defense Against Model Inversion Attacks for Federated Learning in 5G/6G Systems by Sunder A. Khowaja, Parus Khuwaja, Kapal Dev, Keshav Singh, Xingwang Li, Nikolaos Bartzoudis, Ciprian R. Comsa

“…Such attacks can be carried out using generative adversarial networks (GANs), generative models, or by backtracking the model gradients. …”
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