Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
Machine learning approaches to IoT security: A systematic literature review
With the continuous expansion and evolution of IoT applications, attacks on those IoT
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset
The effectiveness of machine learning models can be significantly averse to redundant and
irrelevant features present in the large dataset which can cause drastic performance …
irrelevant features present in the large dataset which can cause drastic performance …
Machine-learning-based DDoS attack detection using mutual information and random forest feature importance method
Cloud computing facilitates the users with on-demand services over the Internet. The
services are accessible from anywhere at any time. Despite the valuable services, the …
services are accessible from anywhere at any time. Despite the valuable services, the …
A new ensemble-based intrusion detection system for internet of things
The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few
years by drastically transforming human lives to automate their ordinary daily tasks. This is …
years by drastically transforming human lives to automate their ordinary daily tasks. This is …
A flow-based anomaly detection approach with feature selection method against ddos attacks in sdns
MS El Sayed, NA Le-Khac, MA Azer… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Software Defined Networking (SDN) is an emerging network platform, which facilitates
centralised network management. The SDN enables the network operators to manage the …
centralised network management. The SDN enables the network operators to manage the …
Troubleshooting an intrusion detection dataset: the CICIDS2017 case study
Numerous studies have demonstrated the effectiveness of machine learning techniques in
application to network intrusion detection. And yet, the adoption of machine learning for …
application to network intrusion detection. And yet, the adoption of machine learning for …
GAN augmentation to deal with imbalance in imaging-based intrusion detection
Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
Semi-supervised spatiotemporal deep learning for intrusions detection in IoT networks
M Abdel-Basset, H Hawash… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
The rapid growth of the Internet of Things (IoT) technologies has generated a huge amount
of traffic that can be exploited for detecting intrusions through IoT networks. Despite the great …
of traffic that can be exploited for detecting intrusions through IoT networks. Despite the great …