Malware poses a severe threat to cyber security. Attackers use malware to achieve their
malicious purposes, such as unauthorized access, stealing confidential data, blackmailing …

Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

The dramatic increase in the number of malware poses a serious challenge to the Android
platform and makes it difficult for malware analysis. In this paper, we propose a novel …

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 …

Machine learning methods can detect Android malware with very high accuracy. However,
these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …

In the ever-changing cyber threat landscape, evolving malware threats demand a new
technique for their detection. This paper puts forward a strategy for distinguishing malware …

The continuous increase and spread of malware have caused immeasurable losses to
social enterprises and even the country, especially unknown malware. Most existing …

Android malware evolution has been neglected by the available data sets, thus providing a
static snapshot of a non-stationary phenomenon. The impact of the time variable has not had …

Learning on execution behaviour, ie, sequences of API calls, is proven to be effective in
malware detection. In this paper, we present CruParamer, a deep neural network based …