7 citations · 9 across the 4 of their papers we have counts for
6 papers
Characterizing Malicious URL Campaigns
Mahathir Almashor, Ejaz Ahmed, Benjamin Pick +4
URLs are central to a myriad of cyber-security threats, from phishing to the distribution of malware. Their inherent ease of use and familiarity is continuously abused by attackers…
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Yansong Gao, Minki Kim, Chandra Thapa +5
Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clien…
DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation
Bedeuro Kim, Sharif Abuadbba, Hyoungshick Kim
Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email dete…
Can the Multi-Incoming Smart Meter Compressed Streams be Re-Compressed?
Sharif Abuadbba, Ayman Ibaida, Ibrahim Khalil +3
Smart meters have currently attracted attention because of their high efficiency and throughput performance. They transmit a massive volume of continuously collected waveform readi…
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
Sharif Abuadbba, Kyuyeon Kim, Minki Kim +5
A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively r…
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
Yansong Gao, Minki Kim, Sharif Abuadbba +6
This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and…