Blockchain (BC) and software-defined networking (SDN) are leading technologies which
have recently found applications in several network-related scenarios and have …

The promise of Deep Learning (DL) in solving hard problems such as network Traffic
Classification (TC) is being held back by the severe lack of transparency and explainability …

Predicting attacks in Android malware devices using machine learning for recommender
systems-based IoT can be a challenging task. However, it is possible to use various …

Current intrusion detection techniques cannot keep up with the increasing amount and
complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to …

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In
recent years various malware detection and classification approaches have been proposed …

In practical scenarios, abnormal network traffic detection often requires analysis of massive,
high-dimensional, and unbalanced data. Popular detection methods waste time by …

In the last few years, Android mobile devices have encountered a large spread and
nowadays a huge part of the traffic traversing the Internet is related to them. In parallel, the …

Network Intrusion Detection Systems are essential tools for protecting networks against
attacks. Deep Learning approaches are increasingly employed in develo** these systems …

Currently, Android is the most widely used mobile operating system globally. This platform
has become a target for malware activities due to its technological and user appeal, open …

With the increasing popularity of Android smartphones, malware targeting the Android
platform is showing explosive growth. Currently, mainstream detection methods use static …