This framework integrates anomaly detection and classification using multi-layer perceptron (MLP) and autoencoder neural network methods. The authors illustrate a Beyond 5G (B5G) scenario where ...

“Anomaly detection is the holy grail of cyber detection where, if you do it right, you don’t need to know a priori the bad thing that you’re looking for,” Bruce Potter, CEO and founder of ...

Additionally, sensor data inherently contains temporal information, making the effective extraction of time-dependent features another key challenge. To address these issues, this paper proposes an ...

Deep learning models for time series anomaly detection are mainly divided into supervised learning, semi-supervised learning, and unsupervised learning. Unsupervised learning has grown in importance ...

Anomaly detection is the process of finding items in a dataset that are different in some way from the majority of the items. For example, you could examine a dataset of credit card transactions to ...

The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent ...