Current machine learning approaches for network-based intrusion detection do not cope
with new network traffic behavior, which requires periodic computationally and time …

Over the last years, several works have proposed highly accurate machine learning (ML)
techniques for network-based intrusion detection systems (NIDS), that are hardly used in …

Network-based intrusion detection is a widely explored topic in the literature. Yet, despite the
promising reported results, designed schemes are rarely used in production environments …

Several works have proposed highly accurate network-based intrusion detection schemes
through machine learning techniques. However, they are unable to address changes in …

Several works have used machine learning techniques for network-based intrusion
detection over the past few years. While proposed schemes have been able to provide high …

Abstract Machine Learning techniques for network-based intrusion detection are widely
adopted in the scientific literature. Besides being highly variable, network traffic behavior …

Abstract Intrusion Detection Systems (IDS) help protect computer networks by identifying
and detecting attempts to obtain unauthorized access to data via computer networks by …

Changes in network traffic behavior over time are neglected by authors who use machine
learning techniques applied to intrusion detection. In general, it is assumed that periodic …

The number of novel attacks observed in networked systems increases every day. Due to
the large amount of generated data over the network, its storage for further analysis may not …

Static machine and deep learning algorithms are commonly used in intrusion detection
systems (IDSs). However, their effectiveness is constrained by the evolving data distribution …