Special Issue "Symmetry-Adapted Machine Learning for Information Security"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editor

Prof. Dr. James (Jong Hyuk) Park
Website
Guest Editor
Department of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Korea
Interests: IoT; human-centric ubiquitous computing; information security; digital forensics; vehicular cloud computing; multimedia computing
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Special Issue Information

Dear Colleagues,

Nowadays, security attacks on information and communication technology (ICT) are increasing. The ever-expanding utilization of the Internet has given rise to various types of new vulnerabilities and approaches to attack computer and communication systems; thus, making computers and network security a major concern. Due to the increasing pervasiveness of modern attacks, many organizations—mainly large commercial organizations—invest over 10% of their total ICT budget directly in network and computer security. The dynamic nature of security attacks—such as data compression, visualization, Advanced Persistent Threat (APT), ransomware, Internet of Things (IoT) attacks, and supply chain attacks—has caused an increased dynamicity of the security threats landscape, making traditional security approaches less efficient.

On the other hand, the symmetry-adapted machine-learning paradigm is an emerging Artificial Intelligence (AI) technology that relies on the extraction and analysis of data to identify hidden patterns of data. It can extract and analyze data from ICT systems over the Internet, which further enables the detection of hidden and new attack patterns to tackle information security threats and challenges. Various machine learning techniques including clustering, association rules, and classification mechanisms can provide effective solutions to generalize and discover attack patterns for handling the recent information security threats such as malicious code, data leak, ransomware, APTs, data compression, etc. Moreover, emerging machine-learning paradigms, such as deep learning and extreme learning machine, can deal with incomplete and inconsistent information using pattern recognition and enable the detection of security attacks with limited computation capacity and lower detection times.

This Special Issue emphasizes the development and application of the machine learning paradigm to handle information security issues in computers and communication systems. We invite original and unpublished works that will contribute the continuing efforts to understand machine learning techniques in the field of information security. Topics of interest include, but are not limited to:

  • Detection of information security threats using symmetry-adapted machine learning
  • Symmetry-adapted machine learning for intrusion detection and prevention
  • Fraud evasion and detection using symmetry-adapted machine learning
  • Symmetry in adversarial machine learning for information security
  • Symmetry-adapted machine learning techniques for identifying information and data leakage
  • Symmetry in sequential learning for information security
  • Symmetry in efficient big data mining to protect against security attacks
  • Innovative deep learning framework for efficient detection of security attacks
  • Symmetry in meta-heuristic paradigm for information security
  • Design and development of intelligent security framework
  • Symmetry in cognitive science for information security using machine learning
  • Monitoring tool to secure symmetric communication network
  • Machine learning for symmetric social networks security

Prof. James (Jong Hyuk) Park
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 papers will be 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. Symmetry is an international peer-reviewed open access monthly 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 1400 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

  • machine learning
  • artificial intelligence
  • deep learning
  • information security
  • internet of things
  • big data security
  • social networking security

Published Papers (13 papers)

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Editorial

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Open AccessEditorial
Symmetry-Adapted Machine Learning for Information Security
Symmetry 2020, 12(6), 1044; https://doi.org/10.3390/sym12061044 - 22 Jun 2020
Abstract
Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for [...] Read more.
Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)

Research

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Open AccessArticle
Malware Classification Using Simhash Encoding and PCA (MCSP)
Symmetry 2020, 12(5), 830; https://doi.org/10.3390/sym12050830 - 19 May 2020
Cited by 1
Abstract
Malware is any malicious program that can attack the security of other computer systems for various purposes. The threat of malware has significantly increased in recent years. To protect our computer systems, we need to analyze an executable file to decide whether it [...] Read more.
Malware is any malicious program that can attack the security of other computer systems for various purposes. The threat of malware has significantly increased in recent years. To protect our computer systems, we need to analyze an executable file to decide whether it is malicious or not. In this paper, we propose two malware classification methods: malware classification using Simhash and PCA (MCSP), and malware classification using Simhash and linear transform (MCSLT). PCA uses the symmetrical covariance matrix. The former method combines Simhash encoding and PCA, and the latter combines Simhash encoding and linear transform layer. To verify the performance of our methods, we compared them with basic malware classification using Simhash and CNN (MCSC) using tanh and relu activation. We used a highly imbalanced dataset with 10,736 samples. As a result, our MCSP method showed the best performance with a maximum accuracy of 98.74% and an average accuracy of 98.59%. It showed an average F1 score of 99.2%. In addition, the MCSLT method showed better performance than MCSC in accuracy and F1 score. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model
Symmetry 2020, 12(2), 203; https://doi.org/10.3390/sym12020203 - 01 Feb 2020
Cited by 3
Abstract
Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety [...] Read more.
Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
SVD-Based Image Watermarking Using the Fast Walsh-Hadamard Transform, Key Mapping, and Coefficient Ordering for Ownership Protection
Symmetry 2020, 12(1), 52; https://doi.org/10.3390/sym12010052 - 26 Dec 2019
Cited by 2
Abstract
Proof of ownership on multimedia data exposes users to significant threats due to a myriad of transmission channel attacks over distributed computing infrastructures. In order to address this problem, in this paper, an efficient blind symmetric image watermarking method using singular value decomposition [...] Read more.
Proof of ownership on multimedia data exposes users to significant threats due to a myriad of transmission channel attacks over distributed computing infrastructures. In order to address this problem, in this paper, an efficient blind symmetric image watermarking method using singular value decomposition (SVD) and the fast Walsh-Hadamard transform (FWHT) is proposed for ownership protection. Initially, Gaussian mapping is used to scramble the watermark image and secure the system against unauthorized detection. Then, FWHT with coefficient ordering is applied to the cover image. To make the embedding process robust and secure against severe attacks, two unique keys are generated from the singular values of the FWHT blocks of the cover image, which are kept by the owner only. Finally, the generated keys are used to extract the watermark and verify the ownership. The simulation result demonstrates that our proposed scheme is highly robust against numerous attacks. Furthermore, comparative analysis corroborates its superiority among other state-of-the-art methods. The NC of the proposed method is numerically one, and the PSNR resides from 49.78 to 52.64. In contrast, the NC of the state-of-the-art methods varies from 0.7991 to 0.9999, while the PSNR exists in the range between 39.4428 and 54.2599. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
Exploration with Multiple Random ε-Buffers in Off-Policy Deep Reinforcement Learning
Symmetry 2019, 11(11), 1352; https://doi.org/10.3390/sym11111352 - 01 Nov 2019
Cited by 1
Abstract
In terms of deep reinforcement learning (RL), exploration is highly significant in achieving better generalization. In benchmark studies, ε-greedy random actions have been used to encourage exploration and prevent over-fitting, thereby improving generalization. Deep RL with random ε-greedy policies, such as deep Q-networks [...] Read more.
In terms of deep reinforcement learning (RL), exploration is highly significant in achieving better generalization. In benchmark studies, ε-greedy random actions have been used to encourage exploration and prevent over-fitting, thereby improving generalization. Deep RL with random ε-greedy policies, such as deep Q-networks (DQNs), can demonstrate efficient exploration behavior. A random ε-greedy policy exploits additional replay buffers in an environment of sparse and binary rewards, such as in the real-time online detection of network securities by verifying whether the network is “normal or anomalous.” Prior studies have illustrated that a prioritized replay memory attributed to a complex temporal difference error provides superior theoretical results. However, another implementation illustrated that in certain environments, the prioritized replay memory is not superior to the randomly-selected buffers of random ε-greedy policy. Moreover, a key challenge of hindsight experience replay inspires our objective by using additional buffers corresponding to each different goal. Therefore, we attempt to exploit multiple random ε-greedy buffers to enhance explorations for a more near-perfect generalization with one original goal in off-policy RL. We demonstrate the benefit of off-policy learning from our method through an experimental comparison of DQN and a deep deterministic policy gradient in terms of discrete action, as well as continuous control for complete symmetric environments. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
A Robust Color Image Watermarking Algorithm Based on APDCBT and SSVD
Symmetry 2019, 11(10), 1227; https://doi.org/10.3390/sym11101227 - 02 Oct 2019
Cited by 1
Abstract
With the wide application of color images, watermarking for the copyright protection of color images has become a research hotspot. In this paper, a robust color image watermarking algorithm based on all phase discrete cosine biorthogonal transform (APDCBT) and shuffled singular value decomposition [...] Read more.
With the wide application of color images, watermarking for the copyright protection of color images has become a research hotspot. In this paper, a robust color image watermarking algorithm based on all phase discrete cosine biorthogonal transform (APDCBT) and shuffled singular value decomposition (SSVD) is proposed. The host image is transformed by the 8 × 8 APDCBT to obtain the direct current (DC) coefficient matrix, and then, the singular value decomposition (SVD) is performed on the DC matrix to embed the watermark. The SSVD and Fibonacci transform are mainly used at the watermark preprocessing stage to improve the security and robustness of the algorithm. The watermarks are color images, and a color quick response (QR) code with error correction mechanism is introduced to be a watermark to further improve the robustness. The watermark embedding and extraction processes are symmetrical. The experimental results show that the algorithm can effectively resist common image processing attacks, such as JPEG compression, Gaussian noise, salt and pepper noise, average filter, median filter, Gaussian filter, sharpening, scaling attacks, and a certain degree of rotation attacks. Compared with the color image watermarking algorithms considered in this paper, the proposed algorithm has better performance in robustness and imperceptibility. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion
Symmetry 2019, 11(6), 761; https://doi.org/10.3390/sym11060761 - 05 Jun 2019
Cited by 1
Abstract
In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the [...] Read more.
In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally established an architecture—3D convolution Two-Stream model based on multi-scale feature fusion. Extensive experimental results on the simulation data show that our network significantly boosts the efficiency of existing convolutional neural networks in the aggregation behavior recognition, achieving the most advanced performance on the dataset constructed in this paper. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network
Symmetry 2019, 11(4), 583; https://doi.org/10.3390/sym11040583 - 22 Apr 2019
Cited by 9
Abstract
With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. [...] Read more.
With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
Design of a Symmetry Protocol for the Efficient Operation of IP Cameras in the IoT Environment
Symmetry 2019, 11(3), 361; https://doi.org/10.3390/sym11030361 - 11 Mar 2019
Cited by 1
Abstract
The rapid development of Internet technology and the spread of various smart devices have enabled the creation of a convenient environment used by people all around the world. It has become increasingly popular, with the technology known as the Internet of Things (IoT). [...] Read more.
The rapid development of Internet technology and the spread of various smart devices have enabled the creation of a convenient environment used by people all around the world. It has become increasingly popular, with the technology known as the Internet of Things (IoT). However, both the development and proliferation of IoT technology have caused various problems such as personal information leakage and privacy violations due to attacks by hackers. Furthermore, countless devices are connected to the network in the sense that all things are connected to the Internet, and network attacks that have thus far been exploited in the existing PC environment are now also occurring frequently in the IoT environment. In fact, there have been many security incidents such as DDoS attacks involving the hacking of IP cameras, which are typical IoT devices, leakages of personal information and the monitoring of numerous persons without their consent. While attacks in the existing Internet environment were PC-based, we have confirmed that various smart devices used in the IoT environment—such as IP cameras and tablets—can be utilized and exploited for attacks on the network. Even though it is necessary to apply security solutions to IoT devices in order to prevent potential problems in the IoT environment, it is difficult to install and execute security solutions due to the inherent features of small devices with limited memory space and computational power in this aforementioned IoT environment, and it is also difficult to protect certificates and encryption keys due to easy physical access. Accordingly, this paper examines potential security threats in the IoT environment and proposes a security design and the development of an intelligent security framework designed to prevent them. The results of the performance evaluation of this study confirm that the proposed protocol is able to cope with various security threats in the network. Furthermore, from the perspective of energy efficiency, it was also possible to confirm that the proposed protocol is superior to other cryptographic protocols. Thus, it is expected to be effective if applied to the IoT environment. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines
Symmetry 2019, 11(2), 165; https://doi.org/10.3390/sym11020165 - 01 Feb 2019
Cited by 7
Abstract
In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, [...] Read more.
In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
A Q-Learning-Based Approach for Deploying Dynamic Service Function Chains
Symmetry 2018, 10(11), 646; https://doi.org/10.3390/sym10110646 - 16 Nov 2018
Cited by 8
Abstract
As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one [...] Read more.
As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It’s necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
Ephemeral ID Beacon-Based Improved Indoor Positioning System
Symmetry 2018, 10(11), 622; https://doi.org/10.3390/sym10110622 - 10 Nov 2018
Cited by 2
Abstract
Recently, the rapid development of mobile devices and communication technologies has dramatically increased the demand for location-based services that provide users with location-oriented information and services. User location in outdoor spaces is measured with high accuracy using GPS. However, because the indoor reception [...] Read more.
Recently, the rapid development of mobile devices and communication technologies has dramatically increased the demand for location-based services that provide users with location-oriented information and services. User location in outdoor spaces is measured with high accuracy using GPS. However, because the indoor reception of GPS signals is not smooth, this solution is not viable in indoor spaces. Many on-going studies are exploring new approaches for indoor location measurement. One popular technique involves using the received signal strength indicator (RSSI) values from the Bluetooth Low Energy (BLE) beacons to measure the distance between a mobile device and the beacons and then determining the position of the user in an indoor space by applying a positioning algorithm such as the trilateration method. However, it remains difficult to obtain accurate data because RSSI values are unstable owing to the influence of elements in the surrounding environment such as weather, humidity, physical barriers, and interference from other signals. In this paper, we propose an indoor location tracking system that improves performance by correcting unstable RSSI signals received from BLE beacons. We apply a filter algorithm based on the average filter and the Kalman filter to reduce the error range of results calculated using the RSSI values. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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Open AccessArticle
Cryptanalysis on SDDO-Based BM123-64 Designs Suitable for Various IoT Application Targets
Symmetry 2018, 10(8), 353; https://doi.org/10.3390/sym10080353 - 20 Aug 2018
Cited by 2
Abstract
BM123-64 block cipher, which was proposed by Minh, N.H. and Bac, D.T. in 2014, was designed for high speed communication applications factors. It was constructed in hybrid controlled substitution–permutation network (CSPN) models with two types of basic controlled elements (CE) in distinctive designs. [...] Read more.
BM123-64 block cipher, which was proposed by Minh, N.H. and Bac, D.T. in 2014, was designed for high speed communication applications factors. It was constructed in hybrid controlled substitution–permutation network (CSPN) models with two types of basic controlled elements (CE) in distinctive designs. This cipher is based on switchable data-dependent operations (SDDO) and covers dependent-operations suitable for efficient primitive approaches for cipher constructions that can generate key schedule in a simple way. The BM123-64 cipher has advantages including high applicability, flexibility, and portability with different algorithm selection for various application targets with internet of things (IoT) as well as secure protection against common types of attacks, for instance, differential attacks and linear attacks. However, in this paper, we propose methods to possibly exploit the BM123-64 structure using related-key attacks. We have constructed a high probability related-key differential characteristics (DCs) on a full eight rounds of BM123-64 cipher. The related-key amplified boomerang attack is then proposed on all three different cases of operation-specific designs with effective results in complexity of data and time consumptions. This study can be considered as the first cryptographic results on BM123-64 cipher. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
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