[HTML][HTML] A systematic literature review of methods and datasets for anomaly-based network intrusion detection
As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …
threatening. Network intrusion detection has been widely accepted as an effective method to …
A survey of network-based intrusion detection data sets
Labeled data sets are necessary to train and evaluate anomaly-based network intrusion
detection systems. This work provides a focused literature survey of data sets for network …
detection systems. This work provides a focused literature survey of data sets for network …
Machine learning methods for cyber security intrusion detection: Datasets and comparative study
The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
Cyber security in smart cities: a review of deep learning-based applications and case studies
D Chen, P Wawrzynski, Z Lv - Sustainable Cities and Society, 2021 - Elsevier
On the one hand, smart cities have brought about various changes, aiming to revolutionize
people's lives. On the other hand, while smart cities bring better life experiences and great …
people's lives. On the other hand, while smart cities bring better life experiences and great …
E-graphsage: A graph neural network based intrusion detection system for iot
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph
Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks …
Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks …
Anomaly-based intrusion detection from network flow features using variational autoencoder
S Zavrak, M Iskefiyeli - IEEE Access, 2020 - ieeexplore.ieee.org
The rapid increase in network traffic has recently led to the importance of flow-based
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …
Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset
In recent years, due to the extensive use of the Internet, the number of networked computers
has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude …
has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude …
CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems
In recent times, Network-based Intrusion Detection Systems (NIDSs) have become very
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …
A survey on data-driven network intrusion detection
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …
classes compared to normal traffic. Many datasets are collected in simulated environments …
Features dimensionality reduction approaches for machine learning based network intrusion detection
The security of networked systems has become a critical universal issue that influences
individuals, enterprises and governments. The rate of attacks against networked systems …
individuals, enterprises and governments. The rate of attacks against networked systems …