This article presents a cloud-based method to classify 0-day attacks from a novel dataset
called UGRansome1819. The primary objective of the research is to classify potential …

As the number of heterogenous IP-connected devices and traffic volume increase, so does
the potential for security breaches. The undetected exploitation of these breaches can bring …

VeriDevOps offers a methodology and a set of integrated mechanisms that significantly
improve automation in DevOps to protect systems at operations time and prevent security …

This Paper uses the CTU-13 dataset, a large and varied database containing network traffic
events, to investigate the use of machine learning (ML) methods for the detection of …

There are numerous methods of identifying network-based attacks using machine learning,
but processing complexity often constrains it to analyses of previously captured traffic to …

Early time series classification (ETSC) is crucial for real-world time-sensitive applications.
This task aims to classify time series data with least timestamps at the desired accuracy …

As the volume of network attacks on Supervisory Control and Data Acquisition (SCADA)
systems increases, the existing supervised methods that over-rely on priori knowledge can …

In an era marked by the escalating architectural complexity of the Internet, network intrusion
detection stands as a pivotal element in cybersecurity. This paper introduces Learn-IDS, an …

Cybersecurity is currently a topic of utmost significance in tech sectors. The ever-evolving
landscape of this field makes it particularly difficult to navigate. This paper aims to help the …

We present a deep-learning (DL) anomaly-based Intrusion Detection System (IDS) for
networked systems, which is able to detect in realtime anomalous network traffic …