A survey and analysis of intrusion detection models based on cse-cic-ids2018 big data
JL Leevy, TM Khoshgoftaar - Journal of Big Data, 2020 - Springer
The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
Implementation of ensemble learning and feature selection for performance improvements in anomaly-based intrusion detection systems
QRS Fitni, K Ramli - … on Industry 4.0, Artificial Intelligence, and …, 2020 - ieeexplore.ieee.org
In recent years, data security in organizational information systems has become a serious
concern. Many attacks are becoming less detectable by firewall and antivirus software. To …
concern. Many attacks are becoming less detectable by firewall and antivirus software. To …
Network intrusion detection using multi-architectural modular deep neural network
R Atefinia, M Ahmadi - The Journal of Supercomputing, 2021 - Springer
The exponential growth of computer networks and the adoption of new network-based
technologies have made computer security an important challenge. With the emergence of …
technologies have made computer security an important challenge. With the emergence of …
Machine-learning-enabled intrusion detection system for cellular connected UAV networks
R Shrestha, A Omidkar, SA Roudi, R Abbas, S Kim - Electronics, 2021 - mdpi.com
The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide
variety of applications in public and private sector from parcel delivery to wildlife …
variety of applications in public and private sector from parcel delivery to wildlife …
Detecting web attacks using random undersampling and ensemble learners
Class imbalance is an important consideration for cybersecurity and machine learning. We
explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 …
explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 …
PBCNN: Packet bytes-based convolutional neural network for network intrusion detection
L Yu, J Dong, L Chen, M Li, B Xu, Z Li, L Qiao, L Liu… - Computer Networks, 2021 - Elsevier
Network intrusion detection system (IDS) protects the target network from the threats of data
breaches and the insecurity of people's privacy. However, most of existing researches on …
breaches and the insecurity of people's privacy. However, most of existing researches on …
Dropout prediction in Moocs using deep learning and machine learning
The nature of teaching and learning has evolved over the years, especially as technology
has evolved. Innovative application of educational analytics has gained momentum. Indeed …
has evolved. Innovative application of educational analytics has gained momentum. Indeed …
Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System
Implantable internet of things medical devices (IoTMD) has drawn a disruptive
transformation in the healthcare domain. It has improved the services of healthcare …
transformation in the healthcare domain. It has improved the services of healthcare …
A hierarchical intrusion detection system based on extreme learning machine and nature-inspired optimization
The surge in cyber-attacks has driven demand for robust Intrusion detection systems (IDSs)
to protect underlying data and sustain availability of network services. Detecting and …
to protect underlying data and sustain availability of network services. Detecting and …
Towards early and accurate network intrusion detection using graph embedding
Early and accurate detection of network intrusions is crucial to ensure network security and
stability. Existing network intrusion detection methods mainly use conventional machine …
stability. Existing network intrusion detection methods mainly use conventional machine …