This paper presents a novel encryption method based on individual block ciphering using Catalan random walks. This paper aims to offer some new possibilities of multimedia data protection to realize ...the rights of participants in the multimedia distribution chain (image, text, video, sound). Also, the emphasis is on advanced analysis of Catalan numbers and their combinatorial representations in multimedia security. The proposed method consists of five phases: conversion, division, selection, encryption, and generation. We presented the application of our method in ensuring the security of multimedia content. The proposed method was implemented in Java. In the experimental testing, we provide the time and space complexity of Catalan keys generation and Maurer’s universal statistical test for the proposed method. Also, we state security analysis using machine learning methods and comparative analysis with existing methods of encrypting data into a blockchain.
This study presents a secret key sharing protocol that establishes cryptographically secured communication between two entities. A new symmetric key exchange scenario for smart city applications is ...presented in this research. The protocol is based on the specific properties of the Fuss-Catalan numbers and the Lattice Path combinatorics. The proposed scenario consists of three phases: generating a Fuss-Catalan object based on the grid dimension, defining the movement in the Lattice Path Grid and defining the key equalisation rules. In the experimental part, the authors present the security analysis of the protocol as well as its test. Also, they examine the equivalence of the proposed with Maurer's satellite scenario and suggest a new scenario that implements an information-theoretical protocol for the public key distribution. Additionally, a comparison with related studies and methods is provided, as well as a comparison with satellite scenario, which proves the advantages of solution presented by the authors. Finally, they propose further research directions regarding key management in smart city applications.
This paper presents a novel iris recognition system based on machine learning methods. The motivation behind this research resides in the interrelatedness of biometric systems and stylometry, as ...shown in our previous research. The main goal of the proposed model is to reach virtually perfect classification accuracy, eliminate false acceptance rates, and cancel the possibility of recreating an iris image from a generated template. To achieve this, we omit Gabor wavelets and other filter banks typically employed in iris recognition systems based on the pioneering work of John Daugman. Instead, we employ machine learning methods that classify biometric templates as numeric features. The biometric templates are generated by converting a normalized iris image into a one-dimensional set of fixed-length codes, which then undergoes stylometric feature extraction. The extracted features are further used for classification. A new recognition method is developed using the CASIA iris database, and its generalizability is demonstrated on the MMU and IITD iris databases separately, and also on their unification with the CASIA database, by applying oversampling before and during the cross-validation procedure. The experimental evaluation shows that the system performs as intended. In addition, the computational costs are significantly decreased with respect to traditional systems, which in turn reduces the overall complexity of the recognition system, making it suitable for use in practical applications.
•Virtually perfect classification with zero false acceptance rates.•Extremely low false rejection rates originating from noise.•Reduced biometric template size to 128 and 256 bits.•Inability to recreate original iris image from template.•Reduced computational costs.
This paper presents a new method of data hiding using Catalan numbers and Dyck words. The proposed steganographic solution belongs to the category of techniques based on the key generating process, ...rather than steganographic techniques such as injecting or substituting bits. The complex stego key consists of three sets of values that provide the technique of a hidden message generation completely. Hidden message is generated based on the data carrier and an adequate complex stego key. An important characteristic of the proposed method is that the data carrier retains its original shape, without supplements or modifications. Proposed method is explained in detail through the general scenario and through concrete examples. State of the art steganographic analysis of the proposed solution is presented in this paper, as well as possible suggestions for application in business information systems, authentication and distribution of secret cryptographic keys.
•Novel data hiding method using Catalan numbers and Dyck words.•Data carrier retains its’ original shape.•Implementation in Java proofs the work.•State of art machine learning analysis proofs the security of the system.
Applying machine learning techniques and methods in biometric recognition has gained significant attention in recent years as it can provide a better performance, high accuracy, and cancellable ...biometrics data. This paper proposes a new approach for fingerprint recognition based on machine learning methods and stylometric features. The proposed solution deals with fingerprint recognition, cancellability, stylometry, blockchain and machine learning. This research uses machine learning methods that classify fingerprint templates as a numeric feature instead of using Gabor wavelets and filters. The proposed method gives very high accuracy for biometric fingerprint templates. For these reasons, we additionally consider the use of an internal blockchain in the form of a distributed database that implements all security services, including privacy protection. Because the recognition method is based on machine learning, the generated templates are a numerical data type and take up minimal memory size, which further favors the application of a blockchain and enables implementation even in IoT devices. We generate the fingerprint biometric template by converting an enhanced fingerprint image into a 1-D set of fixed length codes. After that, we extract stylometric features that will be used for classification. The experiment is conducted on the CASIA-FingerprintV5 and achieved excellent results where the CatBoost method with over-sampling (SMOTE) achieved the best results for All_features(42) and GRRF(10) sets with 99.95% accuracy and 99.98%, respectively, and FAR 0.0007 and 0.0003, respectively. In addition, the proposed system significantly decreased the computational costs which makes it suitable for other applications.
It is not a secret that Internet of Things (IoT) devices often come with not so realistic processing power (i.e. processing power and storage requirements) that would provide a basis for strong ...security and encryption algorithms. This work proposes an approach to adding a simple interface as a security gateway architecture for IoT devices. The security interface provides mapping for the IoT device remote services as long as support for stronger cryptographic algorithms. The solution improves the security of the data that IoT devices send to remote services by performing compatible cryptographic algorithms on the data before it sends to remote services. The result of this work is the development of a security interface that provides support for any cryptographic algorithm, uses Internet Protocol (IP) mapping to prevent access to the devices behind the interface from non-authorized IP addresses. As such it provides robust protection against attacks and data manipulation. The work is tested for memory usage and the strength of the security it provides.
In the paper, the possibility of combining deep neural network (DNN) model compression methods to achieve better compression results was considered. To compare the advantages and disadvantages of ...each method, all methods were applied to the ResNet18 model for pretraining to the NCT-CRC-HE-100K dataset while using CRC-VAL-HE-7K as the validation dataset. In the proposed method, quantization, pruning, weight clustering, QAT (quantization-aware training), preserve cluster QAT (hereinafter PCQAT), and distillation were performed for the compression of ResNet18. The final evaluation of the obtained models was carried out on a Raspberry Pi 4 device using the validation dataset. The greatest model compression result on the disk was achieved by applying the PCQAT method, whose application led to a reduction in size of the initial model by as much as 45 times, whereas the greatest model acceleration result was achieved via distillation on the MobileNetV2 model. All methods led to the compression of the initial size of the model, with a slight loss in the model accuracy or an increase in the model accuracy in the case of QAT and weight clustering. INT8 quantization and knowledge distillation also led to a significant decrease in the model execution time.
This research is of great importance because it applies artificial intelligence methods, more specifically the Random Forest algorithm and the Anfis method to research the key factors that influence ...the success of students in vocational schools. Identifying these influencing factors is not only useful for improving curriculum and practice but also provides valuable guidance to help students master the material more effectively. The main goal of this research is to penetrate deeply into the core of the factors that influence the success of students in vocational schools, using two different methods. Each of the factors represented as input is mutually independent and does not affect each other, but each of them affects the output variable. The parameters considered as input variables are prior programming knowledge and pretest requirements. Then, by finding one factor that has the greatest influence, the factor of pre-exam obligation was investigated in more detail, using the Anfis method, which was broken down into several input parameters. These results emphasize the importance of the combination of the Random Forest algorithm and the ANFIS method in the statistical evaluation and assessment of student achievement in vocational schools. This study provides useful guidelines for improving education and practice in vocational schools to optimize educational outcomes.
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