Machine learning and deep learning methods for intrusion detection systems: A survey
H Liu, B Lang - applied sciences, 2019 - mdpi.com
Networks play important roles in modern life, and cyber security has become a vital research
area. An intrusion detection system (IDS) which is an important cyber security technique …
area. An intrusion detection system (IDS) which is an important cyber security technique …
Deep learning-based intrusion detection systems: a systematic review
Nowadays, the ever-increasing complication and severity of security attacks on computer
networks have inspired security researchers to incorporate different machine learning …
networks have inspired security researchers to incorporate different machine learning …
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 …
Variational LSTM enhanced anomaly detection for industrial big data
With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly
discussed topic in digital and intelligent industry field. The security problem existing in the …
discussed topic in digital and intelligent industry field. The security problem existing in the …
Deep learning methods in network intrusion detection: A survey and an objective comparison
The use of deep learning models for the network intrusion detection task has been an active
area of research in cybersecurity. Although several excellent surveys cover the growing …
area of research in cybersecurity. Although several excellent surveys cover the growing …
Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues
A Aldweesh, A Derhab, AZ Emam - Knowledge-Based Systems, 2020 - Elsevier
The massive growth of data that are transmitted through a variety of devices and
communication protocols have raised serious security concerns, which have increased the …
communication protocols have raised serious security concerns, which have increased the …
A deep learning approach to network intrusion detection
Network intrusion detection systems (NIDSs) play a crucial role in defending computer
networks. However, there are concerns regarding the feasibility and sustainability of current …
networks. However, there are concerns regarding the feasibility and sustainability of current …
Attack classification of an intrusion detection system using deep learning and hyperparameter optimization
A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks
on a network. The success of a NIDS depends on the success of its algorithm and the …
on a network. The success of a NIDS depends on the success of its algorithm and the …
[HTML][HTML] A stacking ensemble of deep learning models for IoT intrusion detection
The number of Internet of Things (IoT) devices has increased considerably in the past few
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …
Protocol-based deep intrusion detection for dos and ddos attacks using unsw-nb15 and bot-iot data-sets
Since its inception, the Internet of Things (IoT) has witnessed mushroom growth as a
breakthrough technology. In a nutshell, IoT is the integration of devices and data such that …
breakthrough technology. In a nutshell, IoT is the integration of devices and data such that …