Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Yeun, Chan Yeob"'
Autor:
Zhang, Zhibo, Li, Pengfei, Hammadi, Ahmed Y. Al, Guo, Fusen, Damiani, Ernesto, Yeun, Chan Yeob
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted att
Externí odkaz:
http://arxiv.org/abs/2401.01896
Metaverse is trending to create a digital circumstance that can transfer the real world to an online platform supported by large quantities of real-time interactions. Pre-trained Artificial Intelligence (AI) models are demonstrating their increasing
Externí odkaz:
http://arxiv.org/abs/2401.01895
Autor:
Zhang, Zhibo, Umar, Sani, Hammadi, Ahmed Y. Al, Yoon, Sangyoung, Damiani, Ernesto, Yeun, Chan Yeob
Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention. However, EEG signal-based risk assessment systems, which could evaluate the emotional states of humans, have shown sev
Externí odkaz:
http://arxiv.org/abs/2302.04224
This paper's main goal is to provide an attacker's point of view on data poisoning assaults that use label-flipping during the training phase of systems that use electroencephalogram (EEG) signals to evaluate human emotion. To attack different machin
Externí odkaz:
http://arxiv.org/abs/2302.04109
Autor:
Zhang, Zhibo, Umar, Sani, Hammadi, Ahmed Y. Al, Yoon, Sangyoung, Damiani, Ernesto, Ardagna, Claudio Agostino, Bena, Nicola, Yeun, Chan Yeob
Publikováno v:
IEEE Access 2023
The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers'
Externí odkaz:
http://arxiv.org/abs/2301.06923
Publikováno v:
Index in journal International Journal of Computer Theory and Engineering (IJCTE), 2023
In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only. The motivation behi
Externí odkaz:
http://arxiv.org/abs/2210.14616
Autor:
Ramirez, Miguel A., Yoon, Sangyoung, Damiani, Ernesto, Hamadi, Hussam Al, Ardagna, Claudio Agostino, Bena, Nicola, Byon, Young-Ji, Kim, Tae-Yeon, Cho, Chung-Suk, Yeun, Chan Yeob
Most recent studies have shown several vulnerabilities to attacks with the potential to jeopardize the integrity of the model, opening in a few recent years a new window of opportunity in terms of cyber-security. The main interest of this paper is di
Externí odkaz:
http://arxiv.org/abs/2210.11592
Autor:
Anisetti, Marco, Ardagna, Claudio A., Balestrucci, Alessandro, Bena, Nicola, Damiani, Ernesto, Yeun, Chan Yeob
Machine learning is becoming ubiquitous. From finance to medicine, machine learning models are boosting decision-making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a c
Externí odkaz:
http://arxiv.org/abs/2209.14013
Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Exp
Externí odkaz:
http://arxiv.org/abs/2209.03166
Publikováno v:
IEEE Access 2022
This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent y
Externí odkaz:
http://arxiv.org/abs/2208.14937