Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
Journals
JCP
Special Issues
Intrusion/Malware Detection and Prevention in Networks—2nd Edition
share announcement

Intrusion/Malware Detection and Prevention in Networks—2nd Edition

A special issue of Journal of Cybersecurity and Privacy (ISSN 2624-800X). This special issue belongs to the section "Security Engineering & Applications".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 456

Special Issue Editors

Dr. Feng Wang

E-Mail Website
Guest Editor
School of Engineering, Liberty University, Lynchburg, VA 24515, USA
Interests: intrusion detection systems; machine learning; cyber security; IoT security and privacy internet measurement
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongning Tang

E-Mail Website
Guest Editor
School Information Technology, Illinois State University, Normal, IL 61790, USA
Interests: network security; artificial intelligence; adaptive learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on the detection of intrusion and malware attacks on communication and networks, future Internet architectures, 5G and beyond wireless networks, enterprises, data centers, edge and cloud networks, software-defined networking (SDN), optical networks, the Internet and IoT-scale networks. We welcome the submission of papers on the following topics:

  • Distributed denial-of-service (DDoS) attack and defense;
  • Explainable prevention strategies;
  • Profiling normal or abnormal system behaviors;
  • Metrics for evaluating the effectiveness of intrusion detection techniques;
  • Access control;
  • Biometrics;
  • Jamming attack and defense;
  • Trojan attack and defense;
  • Viruses and malware;
  • Covert channel detection;
  • Malware and unwanted software

Dr. Feng Wang
Prof. Dr. Yongning Tang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Cybersecurity and Privacy is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • distributed denial-of-service (DDoS) attack and defense
  • explainable prevention strategies
  • profiling normal or abnormal system behaviors
  • metrics for evaluating the effectiveness of intrusion detection techniques
  • access control
  • biometrics
  • jamming attack and defense
  • trojan attack and defense
  • viruses and malware
  • covert channel detection
  • malware and unwanted software

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 1079 KiB  
Article
An Approach for Anomaly Detection in Network Communications Using k-Path Analysis
by Mamadou Kasse, Rodolphe Charrier, Alexandre Berred, Cyrille Bertelle and Christophe Delpierre
J. Cybersecur. Priv. 2024, 4(3), 449-467; https://doi.org/10.3390/jcp4030022 - 19 Jul 2024
Viewed by 246
Abstract
In this paper, we present an innovative approach inspired by the Path-scan model to detect paths with k adjacent edges (k-path) exhibiting unusual behavior (synonymous with anomaly) within network communications. This work is motivated by the challenge of identifying malicious activities [...] Read more.
In this paper, we present an innovative approach inspired by the Path-scan model to detect paths with k adjacent edges (k-path) exhibiting unusual behavior (synonymous with anomaly) within network communications. This work is motivated by the challenge of identifying malicious activities carried out in vulnerable k-path in a small to medium-sized computer network. Each observed edge (time series of the number of events or the number of packets exchanged between two computers in the network) is modeled using the three-state observed Markov model, as opposed to the Path-scan model which uses a two-state model (active state and inactive state), to establish baselines of behavior in order to detect anomalies. This model captures the typical behavior of network communications, as well as patterns of suspicious activity, such as those associated with brute force attacks. We take a perspective by analyzing each vulnerable k-path, enabling the accurate detection of anomalies on the k-path. Using this approach, our method aims to enhance the detection of suspicious activities in computer networks, thus providing a more robust and accurate solution to ensure the security of computer systems. Full article
(This article belongs to the Special Issue Intrusion/Malware Detection and Prevention in Networks—2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop