Journal of Computer Networks and Communications

Advances in Machine Learning for Cybersecurity


Publishing date
01 Oct 2019
Status
Closed
Submission deadline
07 Jun 2019

Lead Editor

1Felician University, New Jersey, USA

2K. N. Toosi University of Technology, Tehran, Iran

3University of New Brunswick, Fredericton, Canada

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

Advances in Machine Learning for Cybersecurity

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

Description

Cybersecurity encompasses the protection of physical and cyber assets from damage and theft, as well as preventing the misdirection or disruption of services provided by electronic means. Damage can occur in several ways and forms, including via code and data injection, network access, or even malpractice by operators. Cybersecurity tasks, based on Gartner’s PPDR model, can be split into five categories: prediction, prevention, detection, response, and monitoring.

Modern cyberattacks utilize sophisticated techniques to bypass security countermeasures. To overcome these in prediction, prevention, detection, and monitoring tasks, state-of-the-art security techniques are integrated with artificial intelligence in order to sense anomalies and model and detect threats. This is achieved by deploying diverse machine learning methods.

Pairing cybersecurity solutions with machine learning is the most advanced approach to identify flaws and weaknesses, especially in large organizations where the large number of devices and users increases the potential for security breaches. This new approach includes turning to big data platforms to extend data accessibility and machine learning for detection of advanced persistent threats. However, this combination does not yet always work perfectly, and it requires continuous adjustments to refine and improve its monitoring capabilities and boost its functionality in finding and mitigating actual breaches before serious damage has occurred.

Potential topics include but are not limited to the following:

  • Machine learning techniques for network intrusion detection
  • Machine learning techniques for phishing detection
  • Machine learning techniques for malware detection
  • Machine learning techniques for spam and fake profile detection in social networks
  • Machine learning techniques for steganalysis and cryptography
  • Machine learning techniques for testing security properties of protocols
  • Machine learning techniques for authentication systems
  • Machine learning techniques for smart meter energy consumption profiling
  • Machine learning techniques for identification and protection of IoT vulnerabilities
  • Machine learning techniques for identifying exploits and zero-day threats

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 6576725
  • - Corrigendum

Corrigendum to “Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network”

Vu Viet Thang | F. F. Pashchenko
  • Special Issue
  • - Volume 2019
  • - Article ID 5124364
  • - Research Article

An Optimized Approach for Secure Data Transmission Using Spread Spectrum Audio Steganography, Chaos Theory, and Social Impact Theory Optimizer

Rohit Tanwar | Kulvinder Singh | ... | Prashant Kumar
  • Special Issue
  • - Volume 2019
  • - Article ID 4754615
  • - Research Article

Spectral Expansion Method for Cloud Reliability Analysis

K. Kotteswari | A. Bharathi
  • Special Issue
  • - Volume 2019
  • - Article ID 5747136
  • - Review Article

A Systematic Literature Review of Authentication in Internet of Things for Heterogeneous Devices

Sanaz Kavianpour | Bharanidharan Shanmugam | ... | Friso De Boer
  • Special Issue
  • - Volume 2019
  • - Article ID 9852472
  • - Research Article

An Efficient Framework for Sharing a File in a Secure Manner Using Asymmetric Key Distribution Management in Cloud Environment

K. V. Pradeep | V. Vijayakumar | V. Subramaniyaswamy
  • Special Issue
  • - Volume 2019
  • - Article ID 7198435
  • - Research Article

A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

San Kyaw Zaw | Sangsuree Vasupongayya
  • Special Issue
  • - Volume 2019
  • - Article ID 4708201
  • - Research Article

Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network

Vu Viet Thang | F. F. Pashchenko
  • Special Issue
  • - Volume 2019
  • - Article ID 8012568
  • - Research Article

Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN)

Myo Myint Oo | Sinchai Kamolphiwong | ... | Sangsuree Vasupongayya
  • Special Issue
  • - Volume 2019
  • - Article ID 2182803
  • - Research Article

A Novel Hybrid Network Traffic Prediction Approach Based on Support Vector Machines

Wenbo Chen | Zhihao Shang | Yanhua Chen
  • Special Issue
  • - Volume 2019
  • - Article ID 4612474
  • - Research Article

Malicious Domain Names Detection Algorithm Based on N-Gram

Hong Zhao | Zhaobin Chang | ... | Xiangyan Zeng
Journal of Computer Networks and Communications
 Journal metrics
Acceptance rate9%
Submission to final decision74 days
Acceptance to publication46 days
CiteScore3.100
Impact Factor-
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