The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits
while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming …

During the last decade, the cybersecurity literature has conferred a high-level role to
machine learning as a powerful security paradigm to recognise malicious software in …

In previous years, cybercriminals have utilized various strategies to evade identification,
including obfuscation, confusion, and polymorphism technology, resulting in an exponential …

The increasing prevalence of network connections is driving a continuous surge in the
requirement for network security and safeguarding against cyberattacks. This has triggered …

Machine learning (ML) is rapidly evolving, leading to numerous theoretical advancements
and widespread applications across multiple fields. The goal of ML is to enable machines to …

Cybercrime threat intelligence enables proactive measures against threat actors and
informed, data-driven security decisions. This study proposes a practical implementation of …

Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control
the operations of the vehicle, improve driving comfort, and safety. For the operation of the …

With the increasing number of malicious PDF files used for cyberattacks, it is essential to
develop efficient and accurate classifiers to detect and prevent these threats. Machine …

Random forest is one of the popular machine learning algorithms used for classification
tasks. In malware detection tasks, random forest can help identify malware with good …

With the increasingly severe challenge of Software Supply Chain (SSC) security, the rising
trend in guarding against security risks has attracted widespread attention. Existing …