Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The
challenge of adapting an anomaly detector to drift in the normal data distribution, especially …

We consider the problem of online learning in the presence of distribution shifts that occur at
an unknown rate and of unknown intensity. We derive a new Bayesian online inference …

Thanks to the inherent spatial or sequential structures underlying the data like images and
texts, deep architectures such as convolutional neural networks (CNNs) and the Transformer …

The ongoing battle between malware distributors and those seeking to prevent the
onslaught of malicious code has, so far, favored the former. Anti-virus methods are faltering …

Adversarial attacks against neural networks in a regression setting are a critical yet
understudied problem. In this work, we advance the state of the art by investigating …

In today's internet world, malware is still the most harmful threat to the internet users. The
new malware developed are distinct from conventional one, more dynamic in design and …

Malicious software (malware) classification offers a unique challenge for continual learning
(CL) regimes due to the volume of new samples received on a daily basis and the evolution …

In this study, we address the challenge of online malware detection for IoT devices. We
propose a method that monitors malware behavior, extracts dynamic features, and converts …

With the increase in the popularity of computerized online applications, the analysis, and
detection of a growing number of newly discovered stealthy malware poses a significant …

Random Simple Recurrent Network (RSRN) is a forecasting model based on the Random
Neural Network (RaNN) and Recurrent Neural Network (RNN). RSRN has demonstrated …