Intrusion detection system (IDS) is one of extensively used techniques in a network topology
to safeguard the integrity and availability of sensitive assets in the protected systems …

Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering
and repealing malicious activities in computer networks. Anomaly-based IDS, in particular …

A pattern matching method (signature-based) is widely used in basic network intrusion
detection systems (IDS). A more robust method is to use a machine learning classifier to …

A Network Intrusion Detection System (NIDS) helps system administrators to detect network
security breaches in their organizations. However, many challenges arise while develo** …

Abstract 'Curse of dimensionality'and the trade-off between low false alarm rate and high
detection rate are the major concerns while designing an efficient intrusion detection system …

This paper presents a novel feature learning model for cyber security tasks. We propose to
use Auto-encoders (AEs), as a generative model, to learn latent representation of different …

Intrusion detection systems do not perform well when it comes to detecting zero-day attacks,
therefore improving their performance in that regard is an active research topic. In this study …

In this paper, we developed a combining classifier model based on tree-based algorithms
for network intrusion detection. The NSL-KDD dataset, a much improved version of the …

Network anomaly detection is one of the most challenging fields in cyber security. Most of
the proposed techniques have high computation complexity or based on heuristic …

This paper proposes an improved detection performance of anomaly-based intrusion
detection system (IDS) using gradient boosted machine (GBM). The best parameters of GBM …