Journal of Cybersecurity and Privacy

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

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Special Issue Editors

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

Prof. 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

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Research

19 pages, 1079 KiB

Open AccessArticle

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 891

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 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)

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