The struggle between security analysts and malware developers is a never-ending battle
with the complexity of malware changing as quickly as innovation grows. Current state-of-the …

Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and
changed over time in various time-series-based tasks, eg weather prediction, network …

Voice interfaces are becoming more ubiquitous and are now the primary input method for
many devices. We explore in this paper how they can be attacked with hidden voice …

Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …

Building machine learning models of malware behavior is widely accepted as a panacea
towards effective malware classification. A crucial requirement for building sustainable …

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be
fooled by the adversarial example, which is crafted via introducing some perturbations into …

Classifiers deployed in the real world operate in a dynamic environment, where the data
distribution can change over time. These changes, referred to as concept drift, can cause the …

Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …

Recent work in security and systems has embraced the use of machine learning (ML)
techniques for identifying misbehavior, eg email spam and fake (Sybil) users in social …

Machine-learning solutions are successfully adopted in multiple contexts but the application
of these techniques to the cyber security domain is complex and still immature. Among the …