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Intrusion, Malware Detection and Prevention in Networks
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Special Issue "Intrusion, Malware Detection and Prevention in Networks"

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 2024 | Viewed by 3058

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
Prof. Dr. Yongning Tang

E-Mail Website
Guest Editor
College of Applied Science & 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.

Published Papers (4 papers)

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Article
Hybrid Machine Learning-Based Approaches for Feature and Overfitting Reduction to Model Intrusion Patterns
by
Fatemeh Ahmadi Abkenari
,
Amin Milani Fard
and
Sara Khanchi
J. Cybersecur. Priv. 2023, 3(3), 544-557; https://doi.org/10.3390/jcp3030026 - 25 Aug 2023
Viewed by 498
Abstract
An intrusion detection system (IDS), whether as a device or software-based agent, plays a significant role in networks and systems security by continuously monitoring traffic behaviour to detect malicious activities. The literature includes IDSs that leverage models trained to detect known attack behaviours. [...] Read more.
An intrusion detection system (IDS), whether as a device or software-based agent, plays a significant role in networks and systems security by continuously monitoring traffic behaviour to detect malicious activities. The literature includes IDSs that leverage models trained to detect known attack behaviours. However, such models suffer from low accuracy or high overfitting. This work aims to enhance the performance of the IDS by making a model based on the observed traffic via applying different single and ensemble classifiers and lowering the classifier’s overfitting on a reduced set of features. We implement various feature reduction techniques, including Linear Regression, LASSO, Random Forest, Boruta, and autoencoders on the CSE-CIC-IDS2018 dataset to provide a training set for classifiers, including Decision Tree, Naïve Bayes, neural networks, Random Forest, and XGBoost. Our experiments show that the Decision Tree classifier on autoencoders-based reduced sets of features yields the lowest overfitting among other combinations. Full article
(This article belongs to the Special Issue Intrusion, Malware Detection and Prevention in Networks)
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Article
A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
by
Humera Ghani
,
Bal Virdee
and
Shahram Salekzamankhani
J. Cybersecur. Priv. 2023, 3(3), 451-463; https://doi.org/10.3390/jcp3030023 - 03 Aug 2023
Viewed by 806
Abstract
With the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. [...] Read more.
With the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. Artificial Intelligence (AI) and Machine Learning (ML) techniques have extensively been employed to identify network anomalies. This paper provides an effective technique to evaluate the classification performance of a deep-learning-based Feedforward Neural Network (FFNN) classifier. A small feature vector is used to detect network traffic anomalies in the UNSW-NB15 and NSL-KDD datasets. The results show that a large feature set can have redundant and unuseful features, and it requires high computation power. The proposed technique exploits a small feature vector and achieves better classification accuracy. Full article
(This article belongs to the Special Issue Intrusion, Malware Detection and Prevention in Networks)
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Article
Hourly Network Anomaly Detection on HTTP Using Exponential Random Graph Models and Autoregressive Moving Average
by
Richard Li
and
Michail Tsikerdekis
J. Cybersecur. Priv. 2023, 3(3), 435-450; https://doi.org/10.3390/jcp3030022 - 01 Aug 2023
Viewed by 430
Abstract
Network anomaly detection solutions can analyze a network’s data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, [...] Read more.
Network anomaly detection solutions can analyze a network’s data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, and Autoregressive Moving Average (ARMA) to analyze that time series and to detect potential attacks. In particular, we extend our previous method in not only demonstrating detection over hourly data but also through labeling of nodes and over the HTTP protocol. We demonstrate the effectiveness of our method using real-world data for creating exfiltration scenarios. We highlight how our method has the potential to provide a useful description of what is happening in the network structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we describe some strengths of our method, its accuracy based on the right selection of parameters, as well as its low computational requirements. Full article
(This article belongs to the Special Issue Intrusion, Malware Detection and Prevention in Networks)
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Article
Mitigating IoT Privacy-Revealing Features by Time Series Data Transformation
by
Feng Wang
,
Yongning Tang
and
Hongbing Fang
J. Cybersecur. Priv. 2023, 3(2), 209-226; https://doi.org/10.3390/jcp3020012 - 18 May 2023
Viewed by 837
Abstract
As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication [...] Read more.
As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations. Full article
(This article belongs to the Special Issue Intrusion, Malware Detection and Prevention in Networks)
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