[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 on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …
between different communicating devices. The data should be communicated securely …
A bidirectional LSTM deep learning approach for intrusion detection
The rise in computer networks and internet attacks has become alarming for most service
providers. It has triggered the need for the development and implementation of intrusion …
providers. It has triggered the need for the development and implementation of intrusion …
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
In this paper, we present a survey of deep learning approaches for cyber security intrusion
detection, the datasets used, and a comparative study. Specifically, we provide a review of …
detection, the datasets used, and a comparative study. Specifically, we provide a review of …
[HTML][HTML] A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework
SM Kasongo - Computer Communications, 2023 - Elsevier
In recent years, the spike in the amount of information transmitted through communication
infrastructures has increased due to the advances in technologies such as cloud computing …
infrastructures has increased due to the advances in technologies such as cloud computing …
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 …
A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges
In recent years, with the rapid development of current Internet and mobile communication
technologies, the infrastructure, devices and resources in networking systems are becoming …
technologies, the infrastructure, devices and resources in networking systems are becoming …
Ddosnet: A deep-learning model for detecting network attacks
MS Elsayed, NA Le-Khac, S Dev… - 2020 IEEE 21st …, 2020 - ieeexplore.ieee.org
Software-Defined Networking (SDN) is an emerging paradigm, which evolved in recent
years to address the weaknesses in traditional networks. The significant feature of the SDN …
years to address the weaknesses in traditional networks. The significant feature of the SDN …
InSDN: A novel SDN intrusion dataset
MS Elsayed, NA Le-Khac, AD Jurcut - IEEE access, 2020 - ieeexplore.ieee.org
Software-Defined Network (SDN) has been developed to reduce network complexity
through control and manage the whole network from a centralized location. Today, SDN is …
through control and manage the whole network from a centralized location. Today, SDN is …
Deep learning and big data technologies for IoT security
Technology has become inevitable in human life, especially the growth of Internet of Things
(IoT), which enables communication and interaction with various devices. However, IoT has …
(IoT), which enables communication and interaction with various devices. However, IoT has …