Deep learning algorithms for cybersecurity applications: A technological and status review
P Dixit, S Silakari - Computer Science Review, 2021 - Elsevier
Cybersecurity mainly prevents the hardware, software, and data present in the system that
has an active internet connection from external attacks. Organizations mainly deploy …
has an active internet connection from external attacks. Organizations mainly deploy …
A survey on data-driven network intrusion detection
D Chou, M Jiang - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …
classes compared to normal traffic. Many datasets are collected in simulated environments …
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 …
A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM
J Liu, Y Gao, F Hu - Computers & Security, 2021 - Elsevier
Network intrusion detection systems play an important role in protecting the network from
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …
Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection
With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …
important to ensure the security of the network. Intrusion detection, as one of the important …
STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment
The ability to process large amounts of data in real time using big data analytics tools brings
many advantages that can be used in intrusion detection systems. Deep learning …
many advantages that can be used in intrusion detection systems. Deep learning …
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 …
Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review
Providing a high-performance Intrusion Detection System (IDS) can be very effective in
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …
An ensemble of prediction and learning mechanism for improving accuracy of anomaly detection in network intrusion environments
The connectivity of our surrounding objects to the internet plays a tremendous role in our
daily lives. Many network applications have been developed in every domain of life …
daily lives. Many network applications have been developed in every domain of life …
Cyber security intrusion detection for agriculture 4.0: machine learning-based solutions, datasets, and future directions
In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber
security. Specifically, we present cyber security threats and evaluation metrics used in the …
security. Specifically, we present cyber security threats and evaluation metrics used in the …