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Applied Sciences
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Application and Research in Network Security Communication Systems
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Application and Research in Network Security Communication Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1756

Special Issue Editors

Dr. Xavier A. Larriva-Novo

E-Mail Website
Guest Editor
ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Interests: telematic systems; cybersecurity; telecommunication
Dr. Andres Marin Lopez

E-Mail Website
Guest Editor
ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Interests: telematic systems; cybersecurity; telecommunication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen a surge in cyberattacks, targeting various weaknesses (people, software, hardware, etc.). This demands more comprehensive cybersecurity solutions that combine diverse technologies to address these evolving threats.

These advanced solutions should encompass all aspects of cybersecurity: prevention, detection, response, and supporting infrastructure. While user awareness training plays a significant role in preventing social engineering attacks, technological solutions can further strengthen a preventative security strategy.

Machine learning algorithms and shared cyber threat intelligence are showing results in detecting anomalies and staying informed about attack methods. Additionally, innovative cryptographic approaches like homomorphic and post-quantum cryptography, alongside blockchain technology, are gaining traction in bolstering various advanced cybersecurity services, particularly when dealing with privacy concerns.

Dr. Xavier A. Larriva-Novo
Dr. Andres Marin Lopez
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • incident response
  • insider attack detection
  • applied post-quantum cryptography
  • cybersecurity taxonomies
  • cybersecurity awareness advance services
  • dynamic risk management
  • interoperable cybersecurity frameworks
  • AI applications to cybersecurity

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Published Papers (2 papers)

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Research

18 pages, 1715 KiB  
Article
Automated Vulnerability Exploitation Using Deep Reinforcement Learning
by Anas AlMajali, Loiy Al-Abed, Khalil M. Ahmad Yousef, Bassam J. Mohd, Zaid Samamah and Anas Abu Shhadeh
Appl. Sci. 2024, 14(20), 9331; https://doi.org/10.3390/app14209331 - 13 Oct 2024
Viewed by 638
Abstract
The main objective of this paper is to develop a reinforcement agent capable of effectively exploiting a specific vulnerability. Automating pentesting can reduce the cost and time of the operation. While there are existing tools like Metasploit Pro that offer automated exploitation capabilities, [...] Read more.
The main objective of this paper is to develop a reinforcement agent capable of effectively exploiting a specific vulnerability. Automating pentesting can reduce the cost and time of the operation. While there are existing tools like Metasploit Pro that offer automated exploitation capabilities, they often require significant execution times and resources due to their reliance on exhaustive payload testing. In this paper, we have created a deep reinforcement agent specifically configured to exploit a targeted vulnerability. Through a training phase, the agent learns and stores payloads along with their corresponding reward values in a neural network. When encountering a specific combination of a target operating system and vulnerability, the agent utilizes its neural network to determine the optimal exploitation options. The novelty of this work lies in employing Deep Reinforcement Learning in vulnerability exploitation analysis. To evaluate our proposed methodology, we conducted training and testing on the Metasploitable platform. The training phase of the reinforcement agent was conducted on two use cases: the first one has one vulnerability, and the second one has four vulnerabilities. Our approach successfully achieved the attacker’s primary objective of establishing a reverse shell with a maximum accuracy of 96.6% and 73.6% for use cases one and two, respectively. Full article
(This article belongs to the Special Issue Application and Research in Network Security Communication Systems)
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20 pages, 2513 KiB  
Article
Extraction of Minimal Set of Traffic Features Using Ensemble of Classifiers and Rank Aggregation for Network Intrusion Detection Systems
by Jacek Krupski, Marcin Iwanowski and Waldemar Graniszewski
Appl. Sci. 2024, 14(16), 6995; https://doi.org/10.3390/app14166995 - 9 Aug 2024
Viewed by 753
Abstract
Network traffic classification models, an essential part of intrusion detection systems, need to be as simple as possible due to the high speed of network transmission. One of the fastest approaches is based on decision trees, where the classification process requires a series [...] Read more.
Network traffic classification models, an essential part of intrusion detection systems, need to be as simple as possible due to the high speed of network transmission. One of the fastest approaches is based on decision trees, where the classification process requires a series of tests, resulting in a class assignment. In the network traffic classification process, these tests are performed on extracted traffic features. The classification computational efficiency grows when the number of features and their tests in the decision tree decreases. This paper investigates the relationship between the number of features used to construct the decision-tree-based intrusion detection model and the classification quality. This work deals with a reference dataset that includes IoT/IIoT network traffic. A feature selection process based on the aggregated rank of features computed as the weighted average of rankings obtained using multiple (in this case, six) classifier-based feature selectors is proposed. It results in a ranking of 32 features sorted by importance and usefulness in the classification process. In the outcome of this part of the study, it turns out that acceptable classification results for the smallest number of best features are achieved for the eight most important features at −95.3% accuracy. In the second part of these experiments, the dependence of the classification speed and accuracy on the number of most important features taken from this ranking is analyzed. In this investigation, optimal times are also obtained for eight or fewer number of the most important features, e.g., the trained decision tree needs 0.95 s to classify nearly 7.6 million samples containing eight network traffic features. The conducted experiments prove that a subset of just a few carefully selected features is sufficient to obtain reasonably high classification accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Application and Research in Network Security Communication Systems)
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