Providing a high-performance Intrusion Detection System (IDS) can be very effective in
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …

Quantum machine learning promises to revolutionize traditional machine learning by
efficiently addressing hard tasks for classical computation. While claims of quantum speed …

Abstract Information is a critical asset in any organization to achieve its strategic goals. For
this, organizations enforce physical, logical, and administrative controls to protect their …

This paper presents a systematic review of deep learning (DL) techniques for Network-
based Intrusion Detection Systems (NIDS) based on Preferred Reporting Items for …

Security of the software system is a prime focus area for software development teams. This
paper explores some data science methods to build a knowledge management system that …

Anomaly detection and its explanation is important in many research areas such as intrusion
detection, fraud detection, unknown attack detection in network traffic and logs. It is …

Cybersecurity has become a field of increasing importance within the past years, with the
National Academy of Engineering most recently designating securing cyberspace as one of …

The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the
potential to improve decision-making, adaptability, and efficiency in dynamic and distributed …

Time-series anomaly detection utilizing deep learning methods is widely used in fraud
detection, network intrusion detection, and medical anomaly detection. Most deep learning …

This paper proposes a model for the task of Acoustic Scene Classification. The proposed
model utilizes convolutional neural networks and a random forest classifier to predict the …