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 …

In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in
a broad range of cyber security applications. Therefore, this paper surveys the existing …

Malware is constantly evolving with rising concern for cyberspace. Deep learning-based
malware detectors are being used as a potential solution. However, these detectors are …

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence
of smart airports. Services and systems powered by the IoT enable smart airports to have …

As cybersecurity detectors increasingly rely on machine learning mechanisms, attacks to
these defenses escalate as well. Supervised classifiers are prone to adversarial evasion …

Since the inception of Andoroid OS, smartphones sales have been growing exponentially,
and today it enjoys the monopoly in the smartphone marketplace. The widespread adoption …

Adversarial machine learning (AML) attacks have become a major concern for organizations
in recent years, as AI has become the industry's focal point and GenAI applications have …

The current study investigates the robustness of deep learning models for accurate medical
diagnosis systems with a specific focus on their ability to maintain performance in the …

The last decade witnessed an exponential growth of smartphones and their users, which
has drawn massive attention from malware designers. The current malware detection …

While a body of studies has been carried out for malware detection with its significance, they
are often limited to known malware patterns due to the reliance on signature-based or …