Nowadays, time series are a widely-exploited methodology to describe phenomena
belonging to different fields. In fact, electrical consumption can be explained, from a data …

Abstract The Internet of Things (IoT) is a key enabler in closing the loop in Cyber-Physical
Systems, providing “smartness” and thus additional value to each monitored/controlled …

The promise of Deep Learning (DL) in solving hard problems such as network Traffic
Classification (TC) is being held back by the severe lack of transparency and explainability …

Current intrusion detection techniques cannot keep up with the increasing amount and
complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to …

Recent advancements in deep learning have shown that multimodal inference can be
particularly useful in tasks like autonomous driving, human health, and production line …

Deep learning (DL) models are frequently employed to extract valuable features from
heterogeneous and high-dimensional healthcare data, which are used to keep track of …

As a core technology of the new generation air traffic management (ATM) system, automatic
dependent surveillance-broadcast (ADS-B) becomes increasingly crucial and its anomaly …

Intrusion Detection Systems (IDS) tackle the challenging task of detecting network attacks as
fast as possible. As this is getting more complex in modern enterprise networks, Artificial …

In vertical federated learning (VFL), commercial entities collaboratively train a model while
preserving data privacy. However, a malicious participant's poisoning attack may degrade …

Semi-supervised encrypted traffic anomaly detection models in zero-positive scenarios are
susceptible to human labeling errors or poisoning attacks, thereby compromising the …