Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Taheri, Rahim"'
Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clie
Externí odkaz:
http://arxiv.org/abs/2403.02983
Autor:
Taheri, Rahim
A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more robust against attacks. They have been proven to be very sensitive to
Externí odkaz:
http://arxiv.org/abs/2204.09502
In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practice avail
Externí odkaz:
http://arxiv.org/abs/2204.07772
The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the field of cyber security have extensively explored the
Externí odkaz:
http://arxiv.org/abs/2204.01690
Publikováno v:
In Internet of Things October 2024 27
Publikováno v:
In Future Generation Computer Systems September 2024 158:28-43
Publikováno v:
In Computers & Security November 2024 146
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments
Externí odkaz:
http://arxiv.org/abs/1908.04473
Autor:
Taheri, Rahim, Ghahramani, Meysam, Javidan, Reza, Shojafar, Mohammad, Pooranian, Zahra, Conti, Mauro
In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest
Externí odkaz:
http://arxiv.org/abs/1908.05759
Publikováno v:
Cluster Computing 2020
The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based ant
Externí odkaz:
http://arxiv.org/abs/1904.09433