Investigating the effect of traffic sampling on machine learning-based network intrusion detection approaches
Machine Learning (ML) based Network Intrusion Systems (NIDSs) operate on flow features
which are obtained from flow exporting protocols (ie, NetFlow). Recent success of ML and …
which are obtained from flow exporting protocols (ie, NetFlow). Recent success of ML and …
Netflow datasets for machine learning-based network intrusion detection systems
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 …
become a promising tool to protect networks against cyberattacks. A wide range of datasets …
Deep reinforcement learning-based traffic sampling for multiple traffic analyzers on software-defined networks
Intrusion detection system (IDS) and deep packet inspection (DPI) are widely used to detect
network attacks and anomalies, thereby enhancing cyber-security. Conventional traffic …
network attacks and anomalies, thereby enhancing cyber-security. Conventional traffic …
How to effectively collect and process network data for intrusion detection?
The number of security breaches in the cyberspace is on the rise. This threat is met with
intensive work in the intrusion detection research community. To keep the defensive …
intensive work in the intrusion detection research community. To keep the defensive …
Robust network intrusion detection system based on machine-learning with early classification
T Kim, W Pak - IEEE Access, 2022 - ieeexplore.ieee.org
Network Intrusion Detection Systems (NIDSs) using pattern matching have a fatal weakness
in that they cannot detect new attacks because they only learn existing patterns and use …
in that they cannot detect new attacks because they only learn existing patterns and use …
Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis
Intrusion detection systems, also known as IDSs, are widely regarded as one of the most
essential components of an organization's network security. This is because IDSs serve as …
essential components of an organization's network security. This is because IDSs serve as …
Machine learning for a network-based intrusion detection system: an application using zeek and the cicids2017 dataset
V Gustavsson - 2019 - diva-portal.org
Cyber security is an emerging field in the IT-sector. As more devices are connected to the
internet, the attack surface for hackers is steadily increasing. Network-based Intrusion …
internet, the attack surface for hackers is steadily increasing. Network-based Intrusion …
Ensemble learning approach for flow-based intrusion detection system
Network security remains a critical issue due to ongoing advancements in Information and
Communication Technologies (ICT) and the concomitant rise in the number of security …
Communication Technologies (ICT) and the concomitant rise in the number of security …
Ai-powered intrusion detection in large-scale traffic networks based on flow sensing strategy and parallel deep analysis
Current intrusion detection systems, which rely on signature-based detection using rules
derived from the inspection of past traffic flows and their signatures, are incapable of …
derived from the inspection of past traffic flows and their signatures, are incapable of …
Towards real-time network intrusion detection with image-based sequential packets representation
J Ghadermazi, A Shah… - IEEE Transactions on Big …, 2024 - ieeexplore.ieee.org
Machine learning (ML) and deep learning (DL) advancements have greatly enhanced
anomaly detection of network intrusion detection systems (NIDS) by empowering them to …
anomaly detection of network intrusion detection systems (NIDS) by empowering them to …