The evolution of recent malicious software with the rising use of digital services has
increased the probability of corrupting data, stealing information, or other cybercrimes by …

There has been an increasing trend of malware release, which raises the alarm for security
professionals worldwide. It is often challenging to stay on top of different types of malware …

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

Dynamic malware detection executes the software in a secured virtual environment and
monitors its run-time behavior. This technique widely uses API sequence analysis to identify …

This paper presents API-MalDetect, a new deep learning-based automated framework for
detecting malware attacks in Windows systems. The framework uses an NLP-based encoder …

Due to the widespread expansion of the Android malware industry, malicious Android
processes mining became a necessity to understand their behavior. Nevertheless, due to …

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 …

Abstract Application Programming Interfaces (APIs) are widely considered a useful data
source for dynamic malware analysis to understand the behavioral characteristics of …

Recently, cybersecurity experts and researchers have given special attention to developing
cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) …

The widespread development of the malware industry is considered the main threat to our e-
society. Therefore, malware analysis should also be enriched with smart heuristic tools that …