Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has
taken a prominent role in the network security management field, due to the substantial …

In this paper, we survey the published work on machine learning-based network intrusion
detection systems covering recent state-of-the-art techniques. We address the problems of …

Abstract Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have
become a promising tool to protect networks against cyberattacks. A wide range of datasets …

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

Deep learning (DL) techniques have recently been proposed for enhancing the accuracy of
network intrusion detection systems (NIDS). However, keeping the DL based detection …

Abstract Network Intrusion Detection Systems (NIDSs) are increasingly crucial due to the
expansion of computer networks. Detection techniques based on machine learning have …

Cyber-security garnered significant attention due to the increased dependency of individuals
and organizations on the Internet and their concern about the security and privacy of their …

With the rapid growth in network-based applications, new risks arise, and different security
mechanisms need additional attention to improve speed and accuracy. Although many new …

The rapid evolution of cyber threats has made traditional intrusion detection systems (IDS)
increasingly ineffective in addressing sophisticated attacks. To combat this challenge, the …