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Recent Advances in Cryptography and Network Security

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 24345

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Special Issue Editors

Prof. Dr. Dae-Ki Kang

E-Mail Website1 Website2
Guest Editor

Machine Learning/Deep Learning Research Labs, Department of Computer Engineering, Dongseo University, Busan 47011, Republic of Korea
Interests: automated machine learning; adversarial machine learning; multi-agent reinforcement learning; few shot learning; generative adversarial network
Special Issues, Collections and Topics in MDPI journals

Dr. Jiacang Ho

E-Mail Website
Co-Guest Editor

IAI Labs and Machine Learning/Deep Learning Research Labs, Department of Computer Engineering, Dongseo University, Busan 47011, Korea
Interests: adversarial machine learning; generative adversarial network; biometric authentication; artificial intelligence

Dr. Dhananjay Singh

E-Mail Website
Co-Guest Editor

ReSESNE Labs, Department of Electronics Engineering, Hankuk (Korea) University of Foreign Studies (HUFS), Seoul 02450, Korea
Interests: future data mobility; connected vehicles; smart city; future internet/5G; IoT; WSN; blockchain; wireless communication; cognitive computing; cybersecurity; artificial intelligence; adaptive security for cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Cryptography and network security focuses on the areas of cryptography and cryptanalysis that include network security, data security, mobile security, cloud security, and endpoint security, which are commonly used to protect users online. However, many researchers have used machine/deep learning techniques to strengthen the network security level. The aim of this Special Issue is to cover all aspects of the latest techniques, including their architectures, operations, and the optimization of their systems. Theoretical and practical developments in the implementation and operation of neural networks in network security, the latest technical reviews, and surveys on network security are welcomed. The papers will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this Special Issue, with only the best high-quality papers selected for publication. The topics of interest for this Special Issue include but are not limited to the following:

Public-key cryptography and RSA;
Adversarial machine learning;
Pseudo-random number generation in cryptography;
Machine learning for cybersecurity;
Artificial intelligence dysfunctions;
Network security application;
Biometric authentication;
Web security;
System security;
Malicious software;
Blockchain technologies;
Adaptive security;
Future data security;
Mobile data security;
Challenges of cybersecurity;
Cybersecurity of communication technologies.

Prof. Dr. Dae-Ki Kang
Dr. Jiacang Ho
Dr. Dhananjay Singh
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. Electronics 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.

Published Papers (7 papers)

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Research

17 pages, 2679 KiB

Open AccessArticle

Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

by Richard Evan Sutanto and Sukho Lee

Electronics 2021, 10(1), 52; https://doi.org/10.3390/electronics10010052 - 30 Dec 2020

Cited by 6 | Viewed by 4677

Abstract

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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16 pages, 5041 KiB

Open AccessArticle

Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

by Alex Ming Hui Wong, Masahiro Furukawa and Taro Maeda

Electronics 2020, 9(12), 2143; https://doi.org/10.3390/electronics9122143 - 14 Dec 2020

Cited by 9 | Viewed by 2631

Abstract

Authentication has three basic factors—knowledge, ownership, and inherence. Biometrics is considered as the inherence factor and is widely used for authentication due to its conveniences. Biometrics consists of static biometrics (physical characteristics) and dynamic biometrics (behavioral). There is a trade-off between robustness and security. Static biometrics, such as fingerprint and face recognition, are often reliable as they are known to be more robust, but once stolen, it is difficult to reset. On the other hand, dynamic biometrics are usually considered to be more secure due to the constant changes in behavior but at the cost of robustness. In this paper, we proposed a multi-factor authentication—rhythmic-based dynamic hand gesture, where the rhythmic pattern is the knowledge factor and the gesture behavior is the inherence factor, and we evaluate the robustness of the proposed method. Our proposal can be easily applied with other input methods because rhythmic pattern can be observed, such as during typing. It is also expected to improve the robustness of the gesture behavior as the rhythmic pattern acts as a symbolic cue for the gesture. The results shown that our method is able to authenticate a genuine user at the highest accuracy of 0.9301 ± 0.0280 and, also, when being mimicked by impostors, the false acceptance rate (FAR) is as low as 0.1038 ± 0.0179. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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14 pages, 423 KiB

Open AccessArticle

Brick Assembly Networks: An Effective Network for Incremental Learning Problems

by Jiacang Ho and Dae-Ki Kang

Electronics 2020, 9(11), 1929; https://doi.org/10.3390/electronics9111929 - 17 Nov 2020

Viewed by 1863

Abstract

Deep neural networks have achieved high performance in image classification, image generation, voice recognition, natural language processing, etc.; however, they still have confronted several open challenges that need to be solved such as incremental learning problem, overfitting in neural networks, hyperparameter optimization, lack of flexibility and multitasking, etc. In this paper, we focus on the incremental learning problem which is related with machine learning methodologies that continuously train an existing model with additional knowledge. To the best of our knowledge, a simple and direct solution to solve this challenge is to retrain the entire neural network after adding the new labels in the output layer. Besides that, transfer learning can be applied only if the domain of the new labels is related to the domain of the labels that have already been trained in the neural network. In this paper, we propose a novel network architecture, namely Brick Assembly Network (BAN), which allows a trained network to assemble (or dismantle) a new label to (or from) a trained neural network without retraining the entire network. In BAN, we train labels with a sub-network (i.e., a simple neural network) individually and then we assemble the converged sub-networks that have trained for a single label together to form a full neural network. For each label to be trained in a sub-network of BAN, we introduce a new loss function that minimizes the loss of the network with only one class data. Applying one loss function for each class label is unique and different from standard neural network architectures (e.g., AlexNet, ResNet, InceptionV3, etc.) which use the values of a loss function from multiple labels to minimize the error of the network. The difference of between the loss functions of previous approaches and the one we have introduced is that we compute a loss values from node values of penultimate layer (we named it as a characteristic layer) instead of the output layer where the computation of the loss values occurs between true labels and predicted labels. From the experiment results on several benchmark datasets, we evaluate that BAN shows a strong capability of adding (and removing) a new label to a trained network compared with a standard neural network and other previous work. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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23 pages, 724 KiB

Open AccessArticle

Efficient Implementation of a Crypto Library Using Web Assembly

by BoSun Park, JinGyo Song and Seog Chung Seo

Electronics 2020, 9(11), 1839; https://doi.org/10.3390/electronics9111839 - 3 Nov 2020

Cited by 4 | Viewed by 2412

Abstract

We implement a cryptographic library using Web Assembly. Web Assembly is expected to show better performance than Javascript. The proposed library provides comprehensive algorithm sets including revised CHAM, Hash Message Authentication Code (HMAC), and ECDH using the NIST P-256 curve to provide confidentiality, data authentication, and key agreement functions. To optimize the performance of revised CHAM in the proposed library, we apply an existing method that is a four-round combining method and additionally propose the precomputation method to CHAM-64/128. The proposed revised CHAM showed an approximate 2.06 times (CHAM-64/128), approximate 2.13 times (CHAM-128/128), and approximate 2.63 times (CHAM-128/256) performance improvement in Web Assembly compared to JavaScript. In addition, CHAM-64/128 applying the precomputation method showed an improved performance by approximately 1.2 times more than the existing CHAM-64/128. For the ECDH using P-256 curve, the naive implementation of ECDH is vulnerable to side-channel attacks (SCA), e.g., simple power analysis (SPA), and timing analysis (TA). Thus, we apply an SPA and TA resistant scalar multiplication method, which is a core operation in ECDH. We present atomic block-based scalar multiplication by revising the previous work. Existing atomic blocks show a performance overhead of 55%, 23%, and 37%, but atomic blocks proposed to use only

P = (X, Y, Z)

show 18%, 6%, and 11% performance overhead. The proposed Web Assembly-based crypto library provides enhanced performance and resistance against SCA thus, it can be used in various web-based applications. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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14 pages, 4502 KiB

Open AccessArticle

Adaptive Wiener Filter and Natural Noise to Eliminate Adversarial Perturbation

by Fei Wu, Wenxue Yang, Limin Xiao and Jinbin Zhu

Electronics 2020, 9(10), 1634; https://doi.org/10.3390/electronics9101634 - 3 Oct 2020

Cited by 18 | Viewed by 4292

Abstract

Deep neural network has been widely used in pattern recognition and speech processing, but its vulnerability to adversarial attacks also proverbially demonstrated. These attacks perform unstructured pixel-wise perturbation to fool the classifier, which does not affect the human visual system. The role of adversarial examples in the information security field has received increased attention across a number of disciplines in recent years. An alternative approach is “like cures like”. In this paper, we propose to utilize common noise and adaptive wiener filtering to mitigate the perturbation. Our method includes two operations: noise addition, which adds natural noise to input adversarial examples, and adaptive wiener filtering, which denoising the images in the previous step. Based on the study of the distribution of attacks, adding natural noise has an impact on adversarial examples to a certain extent and then they can be removed through adaptive wiener filter, which is an optimal estimator for the local variance of the image. The proposed improved adaptive wiener filter can automatically select the optimal window size between the given multiple alternative windows based on the features of different images. Based on lots of experiments, the result demonstrates that the proposed method is capable of defending against adversarial attacks, such as FGSM (Fast Gradient Sign Method), C&W, Deepfool, and JSMA (Jacobian-based Saliency Map Attack). By compared experiments, our method outperforms or is comparable to state-of-the-art methods. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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16 pages, 402 KiB

Open AccessArticle

Designing a CHAM Block Cipher on Low-End Microcontrollers for Internet of Things

by Hyeokdong Kwon, SangWoo An, YoungBeom Kim, Hyunji Kim, Seung Ju Choi, Kyoungbae Jang, Jaehoon Park, Hyunjun Kim, Seog Chung Seo and Hwajeong Seo

Electronics 2020, 9(9), 1548; https://doi.org/10.3390/electronics9091548 - 22 Sep 2020

Cited by 9 | Viewed by 3021

Abstract

As the technology of Internet of Things (IoT) evolves, abundant data is generated from sensor nodes and exchanged between them. For this reason, efficient encryption is required to keep data in secret. Since low-end IoT devices have limited computation power, it is difficult to operate expensive ciphers on them. Lightweight block ciphers reduce computation overheads, which are suitable for low-end IoT platforms. In this paper, we implemented the optimized CHAM block cipher in the counter mode of operation, on 8-bit AVR microcontrollers (i.e., representative sensor nodes). There are four new techniques applied. First, the execution time is drastically reduced, by skipping eight rounds through pre-calculation and look-up table access. Second, the encryption with a variable-key scenario is optimized with the on-the-fly table calculation. Third, the encryption in a parallel way makes multiple blocks computed in online for CHAM-64/128 case. Fourth, the state-of-art engineering technique is fully utilized in terms of the instruction level and register level. With these optimization methods, proposed optimized CHAM implementations for counter mode of operation outperformed the state-of-art implementations by 12.8%, 8.9%, and 9.6% for CHAM-64/128, CHAM-128/128, and CHAM-128/256, respectively. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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12 pages, 594 KiB

Open AccessArticle

Network Anomaly Detection inside Consumer Networks—A Hybrid Approach

by Darsh Patel, Kathiravan Srinivasan, Chuan-Yu Chang, Takshi Gupta and Aman Kataria

Electronics 2020, 9(6), 923; https://doi.org/10.3390/electronics9060923 - 1 Jun 2020

Cited by 23 | Viewed by 3928

Abstract

With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks. Full article

(This article belongs to the Special Issue Recent Advances in Cryptography and Network Security)

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