Results 271 to 280 of about 82,924 (315)

Generative Adversarial Networks for Intrusion Detection Systems: A Comprehensive Survey of Applications, Challenges, and Research Directions. [PDF]

Arab J Sci Eng
Alauthman M, Aslam N, Al-Qerem A, Aldweesh A, Sureephong P.   +4 more
europepmc   +1 more source

Explainable multi agent reinforcement learning framework for secure and adaptive communication in UAV swarm based fanets. [PDF]

Sci Rep
Alkahtani HK, Galiya Y, Akbayan B, Qahmash A.   +3 more
europepmc   +1 more source

Backdoor detection and defense method via parameter-space targeted adversarial perturbations

Tian Youliang, Jin Kunlong, Shi Lujia, Shuai Wang, Zuo Jianshuo, Xiang Axin   +5 more
openalex   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Adversarial Attacks and Defenses on Graphs

ACM SIGKDD Explorations Newsletter, 2021
Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Wei Jin 0009, Yaxin Li 0001, Han Xu 0002, Yiqi Wang 0001, Shuiwang Ji, Charu Aggarwal 0001, Jiliang Tang   +6 more
openaire   +1 more source

Sinkhorn Adversarial Attack and Defense

IEEE Transactions on Image Processing, 2022
Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
openaire   +2 more sources

Randomize Adversarial Defense in a Light Way

2022 IEEE International Conference on Big Data (Big Data), 2022
The ultimate goal in adversarial defense is to build a universally robust defense against all types of attacks, but ongoing arms race between adversarial attacks and defenses show the difficulty in building a deterministic defense to work towards the goal.
Ji-Young Park, Lin Liu 0003, Jixue Liu, Jiuyong Li   +3 more
openaire   +2 more sources

Adversarial Attacks and Defenses

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002, Yaxin Li 0001, Wei Jin 0009, Jiliang Tang   +3 more
openaire   +1 more source