Survey on categorical data for neural networks
JT Hancock, TM Khoshgoftaar - Journal of big data, 2020 - Springer
This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …
to neural networks. Techniques for using qualitative data in neural networks are well known …
A survey of CNN-based network intrusion detection
Over the past few years, Internet applications have become more advanced and widely
used. This has increased the need for Internet networks to be secured. Intrusion detection …
used. This has increased the need for Internet networks to be secured. Intrusion detection …
A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection
The connectivity of devices through the internet plays a remarkable role in our daily lives.
Many network-based applications are utilized in different domains, eg, health care, smart …
Many network-based applications are utilized in different domains, eg, health care, smart …
Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks
Y Yang, K Zheng, C Wu, X Niu, Y Yang - Applied Sciences, 2019 - mdpi.com
Featured Application The model proposed in this paper can be deployed to the enterprise
gateway, dynamically monitor network activities, and connect with the firewall to protect the …
gateway, dynamically monitor network activities, and connect with the firewall to protect the …
Hyperparameter optimization for 1D-CNN-based network intrusion detection using GA and PSO
D Kilichev, W Kim - Mathematics, 2023 - mdpi.com
This study presents a comprehensive exploration of the hyperparameter optimization in one-
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …
网络入侵检测技术综述
蹇诗婕， 卢志刚， 杜丹， 姜波， 刘宝旭 - Journal of Cyber Security, 2020 - jcs.iie.ac.cn
随着互联网时代的发展, 内部威胁, 零日漏洞和DoS 攻击等攻击行为日益增加,
网络安全变得越来越重要, 入侵检测已成为网络攻击检测的一种重要手段. 随着机器学习算法的 …
网络安全变得越来越重要, 入侵检测已成为网络攻击检测的一种重要手段. 随着机器学习算法的 …
Federated learning for network attack detection using attention-based graph neural networks
W Jianping, Q Guangqiu, W Chunming, J Weiwei… - Scientific Reports, 2024 - nature.com
Federated Learning is an effective solution to address the issues of data isolation and
privacy leakage in machine learning. However, ensuring the security of network devices and …
privacy leakage in machine learning. However, ensuring the security of network devices and …
Sign language translation using deep convolutional neural networks
Sign language is a natural, visually oriented and non-verbal communication channel
between people that facilitates communication through facial/bodily expressions, postures …
between people that facilitates communication through facial/bodily expressions, postures …
iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions
Lysine glutarylation is a post-translation modification which plays an important regulatory
role in a variety of physiological and enzymatic processes including mitochondrial functions …
role in a variety of physiological and enzymatic processes including mitochondrial functions …
Data-driven deep learning-based attention mechanism for remaining useful life prediction: Case study application to turbofan engine analysis
Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great
significance for improving the reliability and safety of the engine system. Due to the high …
significance for improving the reliability and safety of the engine system. Due to the high …