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 …

As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …

Deep learning, which is originated from an artificial neural network (ANN), is one of the
major technologies of today's smart cybersecurity systems or policies to function in an …

Internet of Things applications for smart cities have currently become a primary target for
advanced persistent threats of botnets. This article proposes a botnet detection system …

In the field of network security, it is of great significance to accurately detect various types of
Internet of Things (IoT) network intrusion attacks which launched by the attacker-controlled …

In recent years, remote sensing researchers have investigated the use of different modalities
(or combinations of modalities) for classification tasks. Such modalities can be extracted via …

Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

Due to the rising dependency on digital technology, cybersecurity has emerged as a more
prominent field of research and application that typically focuses on securing devices …

Artificial intelligence techniques have grown rapidly in recent years, and their applications in
practice can be seen in many fields, ranging from facial recognition to image analysis. In the …