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Mathematics
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Mathematical Mitigation Techniques for Network and Cyber Security
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Mathematical Mitigation Techniques for Network and Cyber Security

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 24466

Special Issue Editor

Dr. Eckhard Pfluegel

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Kingston University London, Penrhyn Road, Kingston upon Thames KT1 2EE, UK
Interests: computer algebra; computer security; speech user interfaces

Special Issue Information

Dear Colleagues,

The ever-growing importance of networking and cyber security in all areas of technology of modern life has led to an increasing number of novel scientific techniques being proposed, analyzed and evaluated as enhancement or mitigation techniques for distributed, network and cyber security systems and scenarios. Such techniques act either as a means to improve availability, safety, maintainability and usability of the system or else implement security controls that, either proactively or reactively, address internal or external threats as well as vulnerabilities existing in the system.

This special issue "Mathematical Techniques for Network and Cyber Security" invites original high-quality research, leading to enhancement or mitigation techniques based on mathematical approaches for network and cyber security disciplines such as access control, cryptography, honeypots, intrusion detection, mobile security, networking, network security, routing, security protocol design and implementation, security management and assessment, usable security, web security and others.

The published articles will be presenting theoretical or practical results, including novel architectures or systems acting as enhancement or mitigation techniques. They will clearly outline the context of the mathematical technique, its role as enhancement technique or security control, and evaluate its positive impact on the resulting improved system. This will be beneficial to mathematics, computer science, networking and engineering researchers and practitioners alike, to learn about recent trends and their precise application roles within the network and cyber security field.

Dr. Eckhard Pfluegel
Guest Editor

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. Mathematics 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 2600 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 (5 papers)

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Research

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14 pages, 1877 KiB  
Article
Masked Implementation of Format Preserving Encryption on Low-End AVR Microcontrollers and High-End ARM Processors
by Hyunjun Kim, Minjoo Sim, Kyoungbae Jang, Hyeokdong Kwon, Siwoo Uhm and Hwajeong Seo
Mathematics 2021, 9(11), 1294; https://doi.org/10.3390/math9111294 - 04 Jun 2021
Cited by 4 | Viewed by 2015
Abstract
Format-Preserving Encryption (FPE) for Internet of Things (IoT) enables the data encryption while preserving the format and length of original data. With these advantages, FPE can be utilized in many IoT applications. However, FPE requires complicated computations and these are high overheads on [...] Read more.
Format-Preserving Encryption (FPE) for Internet of Things (IoT) enables the data encryption while preserving the format and length of original data. With these advantages, FPE can be utilized in many IoT applications. However, FPE requires complicated computations and these are high overheads on IoT embedded devices. In this paper, we proposed an efficient implementation of Format-preserving Encryption Algorithm (FEA), which is the Korean standard of FPE, and the first-order masked implementation of FEA on both low-end (i.e., AVR microcontroller) and high-end (i.e., ARM processor) IoT devices. Firstly, we show the vulnerability of FEA when it comes to the Correlation Power Analysis (CPA) approach. Afterward, we propose an efficient implementation method and the masking technique for both low-end IoT device and high-end IoT device. The proposed method is secure against power analysis attacks but the performance degradation of masked measure is only 2.53∼3.77% than the naïve FEA implementation. Full article
(This article belongs to the Special Issue Mathematical Mitigation Techniques for Network and Cyber Security)
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16 pages, 802 KiB  
Article
Convolutional Neural Network-Based Cryptography Ransomware Detection for Low-End Embedded Processors
by Hyunji Kim, Jaehoon Park, Hyeokdong Kwon, Kyoungbae Jang and Hwajeong Seo
Mathematics 2021, 9(7), 705; https://doi.org/10.3390/math9070705 - 24 Mar 2021
Cited by 8 | Viewed by 2116
Abstract
A crypto-ransomware has the process to encrypt victim’s files. Afterward, the crypto-ransomware requests a ransom for the password of encrypted files to victims. In this paper, we present a novel approach to prevent crypto-ransomware by detecting block cipher algorithms for Internet of Things [...] Read more.
A crypto-ransomware has the process to encrypt victim’s files. Afterward, the crypto-ransomware requests a ransom for the password of encrypted files to victims. In this paper, we present a novel approach to prevent crypto-ransomware by detecting block cipher algorithms for Internet of Things (IoT) platforms. We extract the sequence and frequency characteristics from the opcode of binary files for the 8-bit Alf and Vegard’s RISC (AVR) processor microcontroller. In other words, the late fusion method is used to extract two features from one source data, learn through each network, and integrate them. We classify the crypto-ransomware virus or harmless software through the proposed method. The general software from AVR packages and block cipher implementations written in C language from lightweight block cipher library (i.e., Fair Evaluation of Lightweight Cryptographic Systems (FELICS)) are trained through the deep learning network and evaluated. The general software and block cipher algorithms are successfully classified by training functions in binary files. Furthermore, we detect binary codes that encrypt a file using block ciphers. The detection rate is evaluated in terms of F-measure, which is the harmonic mean of precision and recall. The proposed method not only achieved 97% detection success rate for crypto-ransomware but also achieved 80% success rate in classification for each lightweight cryptographic algorithm and benign firmware. In addition, the success rate in classification for Substitution-Permutation-Network (SPN) structure, Addition-Rotation-eXclusive-or structures (ARX) structure, and benign firmware is 95%. Full article
(This article belongs to the Special Issue Mathematical Mitigation Techniques for Network and Cyber Security)
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13 pages, 531 KiB  
Article
Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network
by Feng Liu, Xuan Zhou, Xuehu Yan, Yuliang Lu and Shudong Wang
Mathematics 2021, 9(2), 189; https://doi.org/10.3390/math9020189 - 19 Jan 2021
Cited by 16 | Viewed by 2780
Abstract
Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not [...] Read more.
Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance. Full article
(This article belongs to the Special Issue Mathematical Mitigation Techniques for Network and Cyber Security)
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18 pages, 1600 KiB  
Article
GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
by Muhammad K. Shahzad, S. M. Riazul Islam, Mahmud Hossain, Mohammad Abdullah-Al-Wadud, Atif Alamri and Mehdi Hussain
Mathematics 2021, 9(1), 43; https://doi.org/10.3390/math9010043 - 28 Dec 2020
Cited by 18 | Viewed by 2374
Abstract
In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is [...] Read more.
In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency. Full article
(This article belongs to the Special Issue Mathematical Mitigation Techniques for Network and Cyber Security)
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Review

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28 pages, 16294 KiB  
Review
A Review on Text Steganography Techniques
by Mohammed Abdul Majeed, Rossilawati Sulaiman, Zarina Shukur and Mohammad Kamrul Hasan
Mathematics 2021, 9(21), 2829; https://doi.org/10.3390/math9212829 - 08 Nov 2021
Cited by 30 | Viewed by 14154
Abstract
There has been a persistent requirement for safeguarding documents and the data they contain, either in printed or electronic form. This is because the fabrication and faking of documents is prevalent globally, resulting in significant losses for individuals, societies, and industrial sectors, in [...] Read more.
There has been a persistent requirement for safeguarding documents and the data they contain, either in printed or electronic form. This is because the fabrication and faking of documents is prevalent globally, resulting in significant losses for individuals, societies, and industrial sectors, in addition to national security. Therefore, individuals are concerned about protecting their work and avoiding these unlawful actions. Different techniques, such as steganography, cryptography, and coding, have been deployed to protect valuable information. Steganography is an appropriate method, in which the user is able to conceal a message inside another message (cover media). Most of the research on steganography utilizes cover media, such as videos, images, and sounds. Notably, text steganography is usually not given priority because of the difficulties in identifying redundant bits in a text file. To embed information within a document, its attributes must be changed. These attributes may be non-displayed characters, spaces, resized fonts, or purposeful misspellings scattered throughout the text. However, this would be detectable by an attacker or other third party because of the minor change in the document. To address this issue, it is necessary to change the document in such a manner that the change would not be visible to the eye, but could still be decoded using a computer. In this paper, an overview of existing research in this area is provided. First, we provide basic information about text steganography and its general procedure. Next, three classes of text steganography are explained: statistical and random generation, format-based methodologies, and linguistics. The techniques related to each class are analyzed, and particularly the manner in which a unique strategy is provided for hiding secret data. Furthermore, we review the existing works in the development of approaches and algorithms related to text steganography; this review is not exhaustive, and covers research published from 2016 to 2021. This paper aims to assist fellow researchers by compiling the current methods, challenges, and future directions in this field. Full article
(This article belongs to the Special Issue Mathematical Mitigation Techniques for Network and Cyber Security)
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