[HTML][HTML] Cyber risk and cybersecurity: a systematic review of data availability
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …
indicating an increase of more than 50% since 2018. With the average cyber insurance …
[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 …
Machine learning methods for cyber security intrusion detection: Datasets and comparative study
The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system
MA Khan - Processes, 2021 - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …
Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection
JB Awotunde, C Chakraborty… - … and mobile computing, 2021 - Wiley Online Library
The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment
and services to physical systems. The IIoT has been used to generate large quantities of …
and services to physical systems. The IIoT has been used to generate large quantities of …
HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection
W Wang, Y Sheng, J Wang, X Zeng, X Ye… - IEEE …, 2017 - ieeexplore.ieee.org
The development of an anomaly-based intrusion detection system (IDS) is a primary
research direction in the field of intrusion detection. An IDS learns normal and anomalous …
research direction in the field of intrusion detection. An IDS learns normal and anomalous …
Cyber threats to industrial IoT: a survey on attacks and countermeasures
In today's Industrial Internet of Things (IIoT) environment, where different systems interact
with the physical world, the state proposed by the Industry 4.0 standards can lead to …
with the physical world, the state proposed by the Industry 4.0 standards can lead to …
Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection
Handling redundant and irrelevant features in high-dimension datasets has caused a long-
term challenge for network anomaly detection. Eliminating such features with spectral …
term challenge for network anomaly detection. Eliminating such features with spectral …
Identification of malicious activities in industrial internet of things based on deep learning models
Abstract Internet Industrial Control Systems (IICSs) that connect technological appliances
and services with physical systems have become a new direction of research as they face …
and services with physical systems have become a new direction of research as they face …
Building an intrusion detection system using a filter-based feature selection algorithm
Redundant and irrelevant features in data have caused a long-term problem in network
traffic classification. These features not only slow down the process of classification but also …
traffic classification. These features not only slow down the process of classification but also …