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 …

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 …

Despite the promising results of machine learning for network-based intrusion detection,
current techniques are not widely deployed in real-world environments. In general …

Network intrusion detection system (NIDS) adoption is essential for mitigating computer
network attacks in various scenarios. However, the increasing complexity of computer …

Network traffic exhibits a high level of variability over short periods of time. This variability
impacts negatively on the accuracy of anomaly-based network intrusion detection systems …

Machine Learning (ML) techniques have been widely applied for network intrusion
detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer …

Existing machine learning solutions for network-based intrusion detection cannot maintain
their reliability over time when facing high-speed networks and evolving attacks. In this …

Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

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