With the increasing complexity of cyber threats and the expanding scope of cyberspace,
there exist progressively more challenges in cyber threat detection. It's proven that most …

This article explores deep learning models in the field of malware detection in cyberspace,
aiming to provide insights into their relevance and contributions. The primary objective of the …

System-to-system communication via Application Programming Interfaces (APIs) plays a
pivotal role in the seamless interaction among software applications and systems for efficient …

Malware remains a major threat to computer systems, with a vast number of new samples
being identified and documented regularly. Windows systems are particularly vulnerable to …

This paper focuses on the implementation of nfstream, an open source network data
analysis tool and machine learning model using the TensorFlow library for HTTP attack …

This study employed chaotic systems as an innovative approach for shellcode obfuscation to
evade current antivirus detection methods. Standard AV solutions primarily rely on static …

Android malware detection remains a critical issue for mobile security. Cybercriminals target
Android since it is the most popular smartphone operating system (OS). Malware detection …

This study addresses the challenge of predicting airflow volume in under-actuated zones,
where occupant behavior and environmental factors complicate standard models. To …

This article mainly explores how to use optimized deep learning techniques for malware
detection in data-driven mode. A deep learning model was designed, which combines the …

API, in other words system calls are critical data sources for monitoring the operation of
systems and applications, and the data obtained from these calls provides a wealth of …