Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …

The widespread use of the Internet of Things and distributed heterogeneous devices has
shed light on the implementation of efficient and reliable intrusion detection systems. These …

Under the background of Industry 4.0 and smart manufacturing, operators are still the core of
manufacturing production, and the standardization of their actions greatly affects production …

The cyber security field has witnessed several intrusion detection systems (IDSs) that are
critical to the detection of malicious activities in network traffic. In the last couple of years …

The Android platform has become the most popular smartphone operating system, which
makes it a target for malicious mobile apps. This paper proposes a machine learning-based …

Machine learning (ML) is one of the dominating technologies practiced in both the industrial
and academic domains throughout the world. ML algorithms can examine the threats and …

Cybersecurity is a wide term and it is an area which relates with various organizations and
governments, each at different level, usually from solitary to country wise. So, Artificial …

Nowadays, numerous attacks can be considered high risks in terms of the security of
Wireless Sensor Network (WSN). As a result, different applications are introduced to …

Machine learning (ML) is the latest buzzword growing rapidly across the world, and ML
possesses massive potential in numerous domains. ML technology is a subset of Artificial …