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Applied Sciences
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Applications of Enhancing Network Security: Latest Advances and Prospects
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Applications of Enhancing Network Security: Latest Advances and Prospects

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8569

Special Issue Editors

Dr. Marek Pawlicki

E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, Bydgoszcz University of Science and Technology (PBS), 85-796 Bydgoszcz, Poland
Interests: pattern recognition; cybersecurity; AI
Dr. Rafał Kozik

E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, Bydgoszcz University of Science and Technology (PBS), 85-796 Bydgoszcz, Poland
Interests: pattern recognition; cybersecurity; AI

Special Issue Information

Dear Colleagues,

It is the unfortunate reality of living in the modern world that all the entities which capitalise on networking technology—businesses, citizens and sometimes even societies as a whole—are threatened by cyberattacks. The list of cyberthreats is ever-expanding and goes hand in hand with the expansion of networking technologies. The cyber arms race requires constant vigilance from the defence teams and the invention, research, and development of novel security measures, capable of thwarting contemporary attacks.

In this Special Issue, we aim to attract top-tier cybersecurity papers which implement novel security paradigms, approaches, and mechanisms. This includes the utilisation of data analysis, pattern recognition, artificial intelligence, or machine learning.

Relevant topics include but are not limited to the following:

  • Deep and shallow learning in security applications;
  • Hybrid AI models in cybersecurity and anomaly detection;
  • AI methods for threat prediction, detection, and mitigation;
  • Security of the IoT;
  • Security of cloud, fog, and edge networks;
  • Time-series anomaly and intrusion detection methods;
  • Mechanisms for the security of AI itself;
  • Practical applications of AI in security;
  • Mechanisms supporting the explainability of AI utilised in network security.

Dr. Marek Pawlicki
Dr. Rafał Kozik
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

  • pattern recognition
  • cybersecurity
  • AI

Published Papers (4 papers)

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23 pages, 4864 KiB  
Article
Quad Key-Secured 3D Gauss Encryption Compression System with Lyapunov Exponent Validation for Digital Images
by Sharad Salunke, Ashok Kumar Shrivastava, Mohammad Farukh Hashmi, Bharti Ahuja and Neeraj Dhanraj Bokde
Appl. Sci. 2023, 13(3), 1616; https://doi.org/10.3390/app13031616 - 27 Jan 2023
Cited by 1 | Viewed by 1126
Abstract
High-dimensional systems are more secure than their lower-order counterparts. However, high security with these complex sets of equations and parameters reduces the transmission system’s processing speed, necessitating the development of an algorithm that secures and makes the system lightweight, ensuring that the processing [...] Read more.
High-dimensional systems are more secure than their lower-order counterparts. However, high security with these complex sets of equations and parameters reduces the transmission system’s processing speed, necessitating the development of an algorithm that secures and makes the system lightweight, ensuring that the processing speed is not compromised. This study provides a digital image compression–encryption technique based on the idea of a novel quad key-secured 3D Gauss chaotic map with singular value decomposition (SVD) and hybrid chaos, which employs SVD to compress the digital image and a four-key-protected encryption via a novel 3D Gauss map, logistic map, Arnold map, or sine map. The algorithm has three benefits: First, the compression method enables the user to select the appropriate compression level based on the application using a unique number. Second, it features a confusion method in which the image’s pixel coordinates are jumbled using four chaotic maps. The pixel position is randomized, resulting in a communication-safe cipher text image. Third, the four keys are produced using a novel 3D Gauss map, logistic map, Arnold map, or sine map, which are nonlinear and chaotic and, hence, very secure with greater key spaces (2498). Moreover, the novel 3D Gauss map satisfies the Lyapunov exponent distribution, which characterizes any chaotic system. As a result, the technique is extremely safe while simultaneously conserving storage space. The experimental findings demonstrate that the method provides reliable reconstruction with a good PSNR on various singular values. Moreover, the applied attacks demonstrated in the result section prove that the proposed method can firmly withstand the urge of attacks. Full article
(This article belongs to the Special Issue Applications of Enhancing Network Security: Latest Advances and Prospects)
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28 pages, 4528 KiB  
Article
A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
by Anne M. Tall and Cliff C. Zou
Appl. Sci. 2023, 13(2), 1183; https://doi.org/10.3390/app13021183 - 16 Jan 2023
Cited by 4 | Viewed by 3943
Abstract
There is an increasing demand for processing large volumes of unstructured data for a wide variety of applications. However, protection measures for these big data sets are still in their infancy, which could lead to significant security and privacy issues. Attribute-based access control [...] Read more.
There is an increasing demand for processing large volumes of unstructured data for a wide variety of applications. However, protection measures for these big data sets are still in their infancy, which could lead to significant security and privacy issues. Attribute-based access control (ABAC) provides a dynamic and flexible solution that is effective for mediating access. We analyzed and implemented a prototype application of ABAC to large dataset processing in Amazon Web Services, using open-source versions of Apache Hadoop, Ranger, and Atlas. The Hadoop ecosystem is one of the most popular frameworks for large dataset processing and storage and is adopted by major cloud service providers. We conducted a rigorous analysis of cybersecurity in implementing ABAC policies in Hadoop, including developing a synthetic dataset of information at multiple sensitivity levels that realistically represents healthcare and connected social media data. We then developed Apache Spark programs that extract, connect, and transform data in a manner representative of a realistic use case. Our result is a framework for securing big data. Applying this framework ensures that serious cybersecurity concerns are addressed. We provide details of our analysis and experimentation code in a GitHub repository for further research by the community. Full article
(This article belongs to the Special Issue Applications of Enhancing Network Security: Latest Advances and Prospects)
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22 pages, 652 KiB  
Article
SwitchFuzz: Switch Short-Term Goals in Directed Grey-Box Fuzzing
by Ziheng He, Peng Jia, Yong Fang, Yuying Liu and Hairu Luo
Appl. Sci. 2022, 12(21), 11097; https://doi.org/10.3390/app122111097 - 02 Nov 2022
Cited by 1 | Viewed by 1314
Abstract
In recent years, fuzzing has become a powerful tool for security researchers to uncover security vulnerabilities. It is used to discover software vulnerabilities by continuously generating malformed inputs to trigger bugs. Directed grey-box fuzzing has also been widely used in the verification of [...] Read more.
In recent years, fuzzing has become a powerful tool for security researchers to uncover security vulnerabilities. It is used to discover software vulnerabilities by continuously generating malformed inputs to trigger bugs. Directed grey-box fuzzing has also been widely used in the verification of patch testing and in vulnerability reproduction. For directed grey-box fuzzing, the core problem is to make test cases reach the target and trigger vulnerabilities faster. Selecting seeds that are closer to the target site to be mutated first is an effective method. For this purpose, the DGF calculates the distance between the execution path and the target site by a specific algorithm. However, as time elapses in the execution process, the seeds covering a larger amount of basic blocks may be overlooked due to their long distances. At the same time, directed fuzzing often ignores the impact of coverage on test efficiency, resulting in a local optimum problem without accumulating enough valuable test cases. In this paper, we analyze and discuss these problems and propose SwitchFuzz, a fuzzer that can switch short-term goals during execution. SwitchFuzz keeps shortening the distance of test cases to reach the target point when it performs well and prioritizes reaching the target point. When positive feedback is not achieved over a period of time, SwitchFuzz tries to explore more possibilities. We compared the efficiency of SwitchFuzz with that of AFLGO in setting single target and multiple targets for crash recurrence in our experiments, respectively. The results show that SwitchFuzz produces a significant improvement over AFLGO in both the speed and the probability of triggering a specified crash. SwitchFuzz can discover more edges than AFLGO in the same amount of time and can generate seeds with smaller distances. Full article
(This article belongs to the Special Issue Applications of Enhancing Network Security: Latest Advances and Prospects)
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12 pages, 360 KiB  
Article
Towards Zero-Shot Flow-Based Cyber-Security Anomaly Detection Framework
by Mikołaj Komisarek, Rafał Kozik, Marek Pawlicki and Michał Choraś
Appl. Sci. 2022, 12(19), 9636; https://doi.org/10.3390/app12199636 - 26 Sep 2022
Cited by 8 | Viewed by 1681
Abstract
Network flow-based cyber anomaly detection is a difficult and complex task. Although several approaches to tackling this problem have been suggested, many research topics remain open. One of these concerns the problem of model transferability. There is a limited number of papers which [...] Read more.
Network flow-based cyber anomaly detection is a difficult and complex task. Although several approaches to tackling this problem have been suggested, many research topics remain open. One of these concerns the problem of model transferability. There is a limited number of papers which tackle transfer learning in the context of flow-based network anomaly detection, and the proposed approaches are mostly evaluated on outdated datasets. The majority of solutions employ various sophisticated approaches, where different architectures of shallow and deep machine learning are leveraged. Analysis and experimentation show that different solutions achieve remarkable performance in a single domain, but transferring the performance to another domain is tedious and results in serious deterioration in prediction quality. In this paper, an innovative approach is proposed which adapts sketchy data structures to extract generic and universal features and leverages the principles of domain adaptation to improve classification quality in zero- and few-shot scenarios. The proposed approach achieves an F1 score of 0.99 compared to an F1 score of 0.97 achieved by the best-performing related methods. Full article
(This article belongs to the Special Issue Applications of Enhancing Network Security: Latest Advances and Prospects)
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