Security and Communication Networks

Machine Learning for Security and Communication Networks


Publishing date
01 Nov 2021
Status
Closed
Submission deadline
18 Jun 2021

Lead Editor

1Chaoyang University of Technology, Taichung, Taiwan

2Fuzhou University, Fuzhou, China

3National University of Singapore, Singapore

4University of Technology Sydney, Sydney, Australia

This issue is now closed for submissions.
More articles will be published in the near future.

Machine Learning for Security and Communication Networks

This issue is now closed for submissions.
More articles will be published in the near future.

Description

In recent years, supervised machine learning methods (e.g. k nearest neighbours, Bayes' theorem, decision tree, support vector machine, random forest, neural network, convolutional neural network, recurrent neural network, long short-term memory network, gated recurrent unit network), unsupervised machine learning methods (e.g. association rules, k-means, density-based spatial clustering of applications with noise, hierarchical clustering, deep belief networks, deep Boltzmann machine, auto-encoder, de-noising auto-encoder, etc.), reinforcement learning methods (e.g. generative adversarial network, deep Q network, trust region policy optimization, etc.), and federated learning methods have been applied to security and communication networks. For instance, machine learning methods have been used to analyze the behaviours of the data stream in networks and extract the patterns of malicious activities (packet dropping, worm propagation, jammer attacks, etc.) for generating rules in intrusion detection systems. Furthermore, time-series methods (e.g. local outlier factor, cumulative sum, adaptive online thresholding, etc.) have been proposed to retrieve the time-series features of anomalous behaviours for preventing cyber-attacks and malfunctions.

While the area of machine learning methods for security and communication networks is a rapidly expanding field of scientific research, several open research questions still need to be discussed and studied. For instance, using and improving machine learning methods for malicious activity detection, attack detection, mobile endpoint analyses, repetitive security task automation, zero-day vulnerability prevention, and other security applications are important issues in computing and communications.

This Special Issue will solicit papers across various disciplines of security and communication networks in computing and communications. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • New supervised machine learning methods for security and communication networks
  • New unsupervised machine learning methods for security and communication networks
  • New reinforcement learning methods for security and communication networks
  • New federated learning methods for security and communication networks
  • New optimization methods for security and communication networks

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 1439692
  • - Research Article

Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

Yue Shen | Yixin Ren
  • Special Issue
  • - Volume 2021
  • - Article ID 5451820
  • - Research Article

An Adaptive Authenticated Model for Big Data Stream SAVI in SDN-Based Data Center Networks

Qizhao Zhou | Junqing Yu | Dong Li
  • Special Issue
  • - Volume 2021
  • - Article ID 3109473
  • - Research Article

Research on Educational Information Platform Based on Cloud Computing

Ling Fan | Meiyi Xia | ... | Jianmin Hu
  • Special Issue
  • - Volume 2021
  • - Article ID 9275363
  • - Research Article

Design of English Interactive Teaching System Based on Association Rules Algorithm

Aiming Nie
  • Special Issue
  • - Volume 2021
  • - Article ID 9995813
  • - Research Article

A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

Rui Wang | Wanxiong Cai | Zaitang Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 3614291
  • - Research Article

Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm

Lina Huo
  • Special Issue
  • - Volume 2021
  • - Article ID 1014017
  • - Research Article

Design and Reconstruction of Visual Art Based on Virtual Reality

Bai Yun
  • Special Issue
  • - Volume 2021
  • - Article ID 4027900
  • - Research Article

Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network

Chao Wang | Bailing Wang | ... | Hongri Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 3913515
  • - Research Article

Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

Chuan Yue | Lide Wang | ... | Haipeng Yan
  • Special Issue
  • - Volume 2021
  • - Article ID 9294356
  • - Research Article

Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Yuan Liu | Liangfeng Dong
Security and Communication Networks
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Acceptance rate31%
Submission to final decision85 days
Acceptance to publication42 days
CiteScore4.200
Journal Citation Indicator0.370
Impact Factor1.791
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