Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

This survey paper describes a literature review of deep learning (DL) methods for cyber
security applications. A short tutorial-style description of each DL method is provided …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed
serious and evolving security threats to Internet users. To protect legitimate users from these …

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has
become an inseparable part of our daily lives. It is considered as a convenient platform for …

Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment
and services to physical systems. The IIoT has been used to generate large quantities of …

In this paper, we propose a deep learning framework for malware classification. There has
been a huge increase in the volume of malware in recent years which poses a serious …

Abstract Cutting edge Deep Learning (DL) techniques have been widely applied to areas
like image processing and speech recognition so far. Likewise, some DL work has been …