Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-
time data and deploying machine learning models. Nonetheless, an individual IoT device …

The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …

In the ever-evolving landscape of cybersecurity, the proliferation of artificial intelligence (AI)
technologies introduces both promising advancements and daunting challenges. This paper …

The simultaneous development of deep learning techniques and Internet of Things
(IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for …

Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …

Machine learning has become widely adopted as a strategy for dealing with a variety of
cybersecurity issues, ranging from insider threat detection to intrusion and malware …

Ensemble learning is a methodology that integrates multiple DNN learners for improving
prediction performance of individual learners. Diversity is greater when the errors of the …

Deep neural network (DNN) models are known to be vulnerable to maliciously crafted
adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the …