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 …

The struggle between security analysts and malware developers is a never-ending battle
with the complexity of malware changing as quickly as innovation grows. Current state-of-the …

Neural networks provide state-of-the-art results for most machine learning tasks.
Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …

Deep learning takes advantage of large datasets and computationally efficient training
algorithms to outperform other approaches at various machine learning tasks. However …

Deep learning algorithms have been shown to perform extremely well on many classical
machine learning problems. However, recent studies have shown that deep learning, like …

Deep learning models have achieved high performance on many tasks, and thus have been
applied to many security-critical scenarios. For example, deep learning-based face …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

According to the recent studies, malicious software (malware) is increasing at an alarming
rate, and some malware can hide in the system by using different obfuscation techniques. In …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …