Error prevalence in nids datasets: A case study on cic-ids-2017 and cse-cic-ids-2018
Benchmark datasets are heavily depended upon by the research community to validate
theoretical findings and track progression in the state-of-the-art. NIDS dataset creation …
theoretical findings and track progression in the state-of-the-art. NIDS dataset creation …
A hybrid intrusion detection model using ega-pso and improved random forest method
Due to the rapid growth in IT technology, digital data have increased availability, creating
novel security threats that need immediate attention. An intrusion detection system (IDS) is …
novel security threats that need immediate attention. An intrusion detection system (IDS) is …
A secure intrusion detection platform using blockchain and radial basis function neural networks for internet of drones
The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with
“Drones” while retaining incomparable features. Because of its vital applications, IoD …
“Drones” while retaining incomparable features. Because of its vital applications, IoD …
Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach
The constantly evolving digital transformation imposes new requirements on our society.
Aspects relating to reliance on the networking domain and the difficulty of achieving security …
Aspects relating to reliance on the networking domain and the difficulty of achieving security …
On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective
Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …
A novel multi-stage approach for hierarchical intrusion detection
An intrusion detection system (IDS), traditionally an example of an effective security
monitoring system, is facing significant challenges due to the ongoing digitization of our …
monitoring system, is facing significant challenges due to the ongoing digitization of our …
Design and testing novel one-class classifier based on polynomial interpolation with application to networking security
This work exploits the concept of one-class classifier applied to the problem of anomaly
detection in communication networks. The article presents the design of an innovative …
detection in communication networks. The article presents the design of an innovative …
Enhancing intrusion detection in iot communications through ml model generalization with a new dataset (idsai)
One of the fields where Artificial Intelligence (AI) must continue to innovate is computer
security. The integration of Wireless Sensor Networks (WSN) with the Internet of Things (IoT) …
security. The integration of Wireless Sensor Networks (WSN) with the Internet of Things (IoT) …
Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study
Intrusion detection is a primary concern in any modern computer system due to the ever-
growing number of intrusions. Machine learning represents an effective solution to detect …
growing number of intrusions. Machine learning represents an effective solution to detect …
CANET: A hierarchical cnn-attention model for network intrusion detection
Abstract Network Intrusion Detection (NID) is an important defense strategy in modern
networks to detect malicious activities in large-scale cyberspace. The current NID methods …
networks to detect malicious activities in large-scale cyberspace. The current NID methods …