Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Razavi–Far, Roozbeh"'
Autor:
Mohammadian, Hesamodin, Higgins, Griffin, Ansong, Samuel, Razavi-Far, Roozbeh, Ghorbani, Ali A.
Control Flow Graphs and Function Call Graphs have become pivotal in providing a detailed understanding of program execution and effectively characterizing the behavior of malware. These graph-based representations, when combined with Graph Neural Net
Externí odkaz:
http://arxiv.org/abs/2412.03634
Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decision environment to improve its behavior through the reward it receives
Externí odkaz:
http://arxiv.org/abs/2411.10268
Publikováno v:
IEEE Transactions on Big Data, vol. 10, no. 2, pp. 194-213, 2024
Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack sur
Externí odkaz:
http://arxiv.org/abs/2401.17319
Publikováno v:
Int. J. Mach. Learn. & Cyber. (2024)
The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence
Externí odkaz:
http://arxiv.org/abs/2303.08300
Publikováno v:
IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3934-3945, 2022
This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then
Externí odkaz:
http://arxiv.org/abs/2210.06729
Publikováno v:
Federated and Transfer Learning, Springer International Publishing, Cham, pp. 29-55, 2023
The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most
Externí odkaz:
http://arxiv.org/abs/2207.02337
In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimati
Externí odkaz:
http://arxiv.org/abs/2109.10561
Autor:
Zhang, Xichen, Razavi-Far, Roozbeh, Isah, Haruna, David, Amir, Higgins, Griffin, Lu, Rongxing, Ghorbani, Ali A.
Publikováno v:
In Knowledge-Based Systems 7 June 2024 293
