Reviewerly/exHarmony huggingface dataset:
Improving the Performance of Fuzzy Rule-Based Classification Systems Using Particle Swarm Optimization

A fuzzy system can perform well with uncertain data with inefficiency in defining appropriate parameter values of membership function. There exist certain challenges while selecting the fuzzy membership function parameters that can eventually affect the performance of the fuzzy-based classification system. This paper proposes a particle swarm optimization-based fuzzy rule-based system for classification problems. The proposed model focuses on optimizing the membership function parameters using particle swarm optimization. Particle swarm optimization algorithm optimizes the parameters of the triangular membership function to improve the performance of fuzzy rule-based systems. Two datasets—iris and appendicitis—with various combinations of partition type and rule induction algorithms are used to evaluate the proposed model. Along with the proposed model, a simple fuzzy rule-based system without particle swarm optimization is developed, having the same combination of partition type and rule induction algorithm for comparison. In the case of IRIS dataset, using the combination of hierarchical fuzzy partition, prototyping and Wang–Mendel rule induction algorithm, the developed fuzzy rule-based system attained an accuracy of 96.86%, 96.86% and 97.77% for 10, 20 and 50 particle sizes, respectively. While fuzzy rule-based system using k-means fuzzy partition, prototyping and fuzzy decision tree rule induction algorithm achieved an accuracy of 91.67%, 91.89% and 92.140% for APPENDICITIS dataset with 10, 20 and 50 particles, respectively. The experimental result shows that a particle swarm optimization-based fuzzy rule-based system could significantly improve the accuracy compared to a simple fuzzy rule-based system.
Safely Sending School Grades Using Quick Response Code

In recent years, the use of technology has increased rapidly as many people send a lot of text or image information every day, and also sometimes in schools or different study centers, teachers need to send private information such as grades to their students or their parents. In addition, this information is generally for a particular student, so it is necessary to implement some tool that helps them send secure information through to the Internet. And in these cases, encryption plays a fundamental role in security. On the other hand, the quick response (QR) code is used widely to provide easy access to information, and also it could be applied for secured communications using different devices to access the internet. For that reason, this paper proposes a new method to send secure information about students’ grades using a QR code, and in order to keep the information safe, we have proposed to use $$3\times 3$$ patterns to represent any number and the information into the QR code which has been distributed in random positions whose average variation percentage was 1.142857143, which is within the margin of error allowed in QR codes. Finally, the experiments have demonstrated that the proposed method can achieve successful results and each student with the correct password can be able to retrieve only their grades.
mCD and Clipped RBM-Based DBN for Optimal Classification of Breast Cancer

Breast cancer is amongst the deadliest forms of cancer, posing a serious danger to the health of a large percentage of women throughout the world. Although researchers all across the world have provided variational approaches for screening this disease, the existing approaches still require further refining to ensure proper and adequate monitoring of this disease. Deep models have recently been shown to be extremely strong generative models capable of extracting features automatically and achieving great prediction performance. The main pretension of this work is to introduce an approach for classifying the breast cancer histopathology images using a deep learning (DL) algorithm. In this work, we proposed a new modified contrastive divergence (CD) algorithm with clipped restricted Boltzmann machine (RBM) which overcomes the problems related to existing methodologies. As contrasted to conventional (standard) CD, it prompts a consistent estimate and lower bias which is very well suited to building a decent model. In comparison to existing methods, the proposed (suggested) method achieves 97.68%.
An MLP Neural Network for Approximation of a Functional Dependence with Noise

Multilayer perceptron (MLP) neural networks used for approximation of the functional dependency are capable of generalization and thus to a limited noise removal, for example from measured data. The following text shows the effect of noise on the results obtained when data is interpolated by a neural network on several functions of two and one function of three variables. The function values obtained from the trained neural network showed on average ten times lower deviations from the correct value than the data on which the network was trained, especially for higher noise levels. The obtained results confirm the suitability of using a neural network for an interpolation of unknown functional dependencies from measured data, even when the noise load cannot be removed.
Mixed-Language Sentiment Analysis on Malaysian Social Media Using Translated VADER and Normalisation Heuristics

Most work in Sentiment Analysis has so far been in a single-language context, primarily English. This work addresses the neglected issue of Sentiment Analysis in a mixed-language environment: Malaysian Social Media, which freely combines both Malay and English. The highly-cited and effective English Sentiment Analysis system VADER was converted to Malay for the first time and used in combination with English VADER to create a multi-language Sentiment Analysis system. Significant patterns in noisy Malaysian Social Media text were identified and heuristics for normalising them were devised. Mixed-language VADER with normalisation heuristics was able to achieve a 12% improvement in accuracy as compared to Malay VADER alone. In absolute terms, performance must be improved, but the results obtained here are encouraging for the future continuation of this approach.
Blockchain-Based Secure and Energy-Efficient Healthcare IoT Using Novel QIRWS-BWO and SAES Techniques

To monitor the patients’ medical status, various algorithms have been developed. Nevertheless, major complications are higher energy consumption along with security. Thus, by utilizing novel Quadratic Interpolation and Roulette Wheel Selection Black Widow Optimization (QIRWS-BWO) and Swapping-centric Advanced Encryption Standard (SAES) methodologies, a Blockchain (BC)-centered security and energy-efficient measure have been proposed in healthcare IoT. Firstly, by utilizing Binary Streebog Hashing Algorithm (BSHA), patients undergo registration together; the hash code will be generated during registration. Then, by employing the Spearman Rho Correlation Coefficient Gaussian Mixture Model (SRCGMM), the patients’ nodes are clustered. Then, by utilizing the QIRWS-BWO, the cluster heads (CHs) are chosen. Afterward, by SAES, the data are encrypted to access the data. The outcomes displayed that when analogized with the prevailing methodologies, the proposed model shows better performance.
SmartTour: A Blockchain-Based Smart Tourism Platform Using Improvised SHA

Technological solutions are immediately needed in the conventional tourism sector to reduce costs and increase productivity. Smart tourism offers a reliable framework to connect the tourism firm and the visitors using blockchain technology which is a new innovation that has the potential to revolutionize the travel and tourism market. The earlier blockchain-based tourism systems, unfortunately, are either hypothetical or incapable of addressing the core issues facing the travel industry. In this paper, we propose SmartTour, a smart tourism application built on the blockchain technology with an improvised SHA to deal with the issues and a working concept. SmartTour’s general system structure is specifically created to establish a reliable connection between visitors and their destinations. Additionally, the visitor can give feedback once after he checks in to the attraction by using the one-time password (OTP).
Computer Vision-Based Electrical Equipment Condition Monitoring and Component Identification

The constant use of electrical equipment for years makes it necessary to maintain them in good condition for proper working, and this is mostly done at the risk of the people in charge of maintenance. The key factor for electrical accidents is the lack of maintenance of the electrical equipment, which eventually results in injuries, fatalities to people and monetary loss. The proposed solution is to reduce such hazards caused to human life and property. In this work, the electrical equipment is continuously monitored, internal faults are identified using thermal images and the changes in the heat spread are analyzed. The maximum prediction accuracy of the proposed methodology is 97.47%. The components of the equipment are labeled and significant physical defects are detected in the images captured using a normal camera through image processing using You Only Look Once-version 3 (YOLO-V3). In this way, computer vision and image processing can be utilized to make the preventive/maintenance mechanisms smarter.
Impact of Dimensionality Reduction on Membership Privacy of CNN Models

Dimensionality reduction is an essential tool for exploratory data analysis, classification and clustering, manifold learning, and preprocessing in deep learning. The curse of dimensionality is a well-recognized serious problem in machine learning. Many researchers have attempted to improve machine learning accuracy and performance with dimensionality reduction. Nowadays, machine learning models’ privacy vulnerability is an essential quality measure. The effect of dimensionality reduction on the privacy leakage of deep learning models is an understudied research area. This work explored the effects of dimensionality reduction on the privacy leakage of the deep learning model. The experiments for image classification using CNN model were performed on the widely used Cifar10 dataset. The results show that although the PCA technique improves the classification task, it does not enhance the model’s privacy when tested against the membership inference attack.
Physical Unclonable Function and Smart Contract-Based Authentication Protocol for Medical Sensor Network

In wireless medical sensor networks (WMSN), a group of nodes to collect a patient's bio-data has revolutionized the existing medical paradigm, allowing medical professionals to access the patient's record remotely and timely. However, the use of public communication channels and a centralized server to store the vital information of all the patients incurs greater risk to the patient's privacy and also weakens the framework due to one centralized point of failure. Numerous researchers have proposed secure key agreement and authentication protocols but most of these protocols fail to withstand physical level attacks or they tend to incur added computation costs and increased number of bits being transmitted over the communication channel. IoT devices being resource-constrained need a lightweight and robust security mechanism without draining their already limited resources. To overcome these issues, in this paper, the proposed scheme integrates blockchain and the PUF-assisted approach for WMSN. The proposed approach makes use of PUF and the challenge-response pair generated via it to produce a secure and unclonable session key without the need to store any public keys. The comparison study done later in the paper provides a comparison on the basis of number of bits being transmitted and number of hash functions being used, between the existing protocols and the proposed one. In the end, the formal security analysis done using the Scyther tool indicates the efficiency and reliability of the proposed scheme.
Computational Results on Gowers $$U_2$$ and $$U_3$$ Norms of Known S-Boxes

The $$(r + 1)$$ th order Gowers norm of a Boolean function is a measure of its resistance to rth order approximations. Gowers, Green, and Tao presented the connection between Gowers uniformity norm of a function and its correlation with polynomials of a certain degree. Gowers $$U_2$$ and $$U_3$$ norms measure the resistance of a Boolean function against linear and quadratic approximations, respectively. This paper presents computational results on the Gowers $$U_2$$ and $$U_3$$ norms of some known 4, 5, and 6-bit S-Boxes used in the present-day block ciphers. It is observed that there are S-Boxes having the same algebraic degree, differential uniformity, and linearity, but their Gowers norms values are different. Equivalently, they possess different strengths against linear and quadratic cryptanalysis.
Cryptologists Should Not Ignore the History of Al-Andalusia

Most cryptographers believe our modern systems and proven secure protocols cannot be broken. Some are now convincing Treasure Departments to make their own versions of Bitcoin. Today cryptosystems are considered secure as long as academics have not broken them. Earlier we argued that this approach might be badly flawed, by presenting a minority viewpoint. In this paper, we look at what the history of Al-Andalusia might teach us in regard to our research community. We also wonder how "open" our so called "open" research is.
Searching for Gemstones: Flawed Stegosystems May Hide Promising Ideas

The historical domain of information hiding is alternatively used nowadays for communication security. Maybe the oldest and certainly one of the most studied field that falls in this category is steganography. Within the current paper, we focus on image steganography techniques in the case of the JPEG format. We propose a corrected and optimized version of the J3 stegosystem which, to the best of our knowledge and as shown throughout the paper, may be considered a very good choice in terms of security and efficiency as compared to current solutions. We reconstruct the entire J3 algorithm (pre-processing, message insertion and extraction) and detail all the modifications. We also present implementation optimizations and cover all practical cases while still using the maximum image insertion capacity.
Symmetric 4-Adic Complexity of Quaternary Generalized Cyclotomic Sequences of Order Four with Period $$2p^n$$

4-adic complexity is an important characteristic of the unpredictability of a sequence. It is defined as the smallest order of feedback with carry shift register that can generate the whole sequence. In this paper, we estimate the symmetric 4-adic complexity of two classes of quaternary sequences with period $$2p^n$$ . These sequences are constructed on generalized cyclotomic classes of order four and have a high linear complexity over the finite ring and over the field of order four. We show that the 4-adic complexity is good enough to resist the attack of the rational approximation algorithm.
Malicious JavaScript Detection Based on AST Analysis and Key Feature Re-sampling in Realistic Environments

Malicious JavaScript detection using machine learning models has shown many great results over the years. However, real-world data only has a small fraction of malicious JavaScript, making it an imbalanced dataset. Many of the previous techniques ignore most of the benign samples and focus on training a machine learning model with a balanced dataset. This paper proposes a Doc2Vec-based filter model that can quickly classify JavaScript malware using Natural Language Processing (NLP) and feature re-sampling. The feature of the JavaScript file will be converted into vector form and used to train the classifiers. Doc2Vec, a NLP model used for documents is used to create feature vectors from the datasets. In this paper, the total features of the benign samples will be reduced using a combination of word vector and clustering model. Random seed oversampling will be used to generate new training malicious data based on the original training dataset. We evaluate our models with a dataset of over 30,000 samples obtained from top popular websites, PhishTank, and GitHub. The experimental result shows that the best f1-score achieves at 0.99 with the MLP classifier.
Forecasting Click Fraud via Machine Learning Algorithms

Click fraud, the manipulation of online advertisement traffic figures, is becoming a major concern for businesses that advertise online. This can lead to financial losses and inaccurate click statistics. To address this problem, it is essential to have a reliable method for identifying click fraud. This includes distinguishing between legitimate clicks made by users and fraudulent clicks generated by bots or other software, which enables companies to advertise their products safely. The XGBoost model was trained on the TalkingData AdTracking Fraud Detection dataset from Kaggle, using binary classification to predict the likelihood of a click being fraudulent. The importance of data treatment was taken into consideration in the model training process, by carefully preprocessing and cleaning the data before feeding it into the model. This helped to improve the accuracy and performance of the model by reaching an AUC of 0.96 and LogLoss of 0.15.
Moderate Classical McEliece Keys from Quasi-Centrosymmetric Goppa Codes

As the development of quantum machines is booming and would threaten our standard cryptography algorithms, a transition period is necessary for the protection of the data processed by our classical machines as well before the arrival of theses machines as after. Recently, to get ahead of the curve, the National Institute of Standards and Technology (NIST) launched the Post Quantum Cryptography Standardization Project, started since late 2016. Among finalists, 3 promising code-theoretic finalist candidates, Classic McEliece, BIKE, and HQC are sent to the fourth round. In this work, to reduce classical McEliece key size without loss of security, we present a new key generation algorithm by introducing new family of codes called quasi-centrosymmetric Goppa codes with a moderate key size for storage optimisation. We also have characterized these codes in the case where the parity matrix is in Cauchy form by giving an algorithm to build them. We ended up giving a detailed analysis of the security against the most known structural attacks by giving the new complexities.
Lattice-Based Accumulator with Constant Time List Update and Constant Time Verification

An accumulator is a cryptographic protocol that compresses a set of inputs into a short string of a certain size and can efficiently prove that the compressed set contains a particular input element. Accumulators have been actively studied in recent years and are used to streamline various protocols such as membership rosters, zero-knowledge proofs, group signatures, and blockchains. Libert et al. proposed a Merkle tree-based accumulator using lattice cryptography, one of the post-quantum cryptography. They proposed an accumulator with logarithmic time complexity for the verification algorithm. Ling et al. proposed an accumulator that satisfies logarithmic time updating lists. However, no algorithm has been proposed thus far that satisfies constant time updating lists and constant time verification based on the lattice-based accumulator. In this study, we propose an accumulator based on lattice that satisfies constant-time verification and constant-time updating lists for the first time. In our proposed accumulator, the bit length of the witness associated with each element is independent of the number of elements in the list. We developed techniques that use the Partial Fourier Recovery problem instead of the Merkle tree. We also prove that the proposed accumulator satisfies the security requirements of an accumulator scheme. Finally, to demonstrate that our proposed accumulator is more practical, we compared it with other lattice-based accumulators. The proposed accumulator scheme can be incorporated into membership list management, zero-knowledge proof, group signature, and blockchain to realize more efficient applications.
A Side-Channel Secret Key Recovery Attack on CRYSTALS-Kyber Using k Chosen Ciphertexts

At CHES'2021, a chosen ciphertext attack combined with belief propagation which can recover the long-term secret key of CRYSTALS-Kyber from side-channel information of the number theoretic transform (NTT) computations was presented. The attack requires k traces from the inverse NTT step of decryption, where k is the module rank, for a noise tolerance $$\sigma \le 1.2$$ in the Hamming weight (HW) leakage on simulated data. In this paper, we present an attack which can recover the secret key of CRYSTALS-Kyber from k chosen ciphertexts using side-channel information of the Barret reduction and message decoding steps of decryption, for $$k \in \{3,4\}$$ . The key novel idea is to create a unique mapping between the secret key coefficients and multiple intermediate variables of these procedures. The redundancy in the mapping patterns enables us to detect errors in the secret key coefficients recovered from side-channel information. We demonstrate the attack on the example of a software implementation of Kyber-768 in ARM Cortex-M4 CPU using deep learning-based power analysis.
Full Post-Quantum Datagram TLS Handshake in the Internet of Things

Quantum computers are a threat to the current standards for secure communication. The Datagram Transport Layer Security (DTLS) protocol is a common protocol used by Internet of Things (IoT) devices that will be broken by such computers. Although quantum computers are yet to become commercially available, IoT devices are generally long-lived. Thus the transition to quantum secure cryptography, as soon as possible, is necessary. IoT devices are generally resource-constrained and Post-Quantum (PQ) cryptography is often more resource intensive computationally compared to current cryptographic standards, adding to the complexity of the transition. In this paper, we propose a PQ version of DTLS 1.3 in IoT, at some additional costs. We first identify a suitable PQ digital signature scheme and Key Encapsulation Mechanism (KEM) to be used in a PQ version of the DTLS protocol. Using the selected PQ algorithms, we implement and evaluate a full PQ DTLS 1.3 handshake on a Raspberry Pi 4B. We find that CPU usage is actually lower compared to current cryptographic schemes used in DTLS 1.3. We notice a significant increase of up to 6x as many packets sent when establishing a connection, depending on the security level. Moreover, memory usage is significantly greater, requiring at least an extra 800 KiB of memory to connect 100 devices.
A Study for Security of Visual Cryptography

We verify the achievement of information-theoretic security of Visual Cryptography (VC) based on the detailed attack scenario. In addition, practical VCs use pseudo-random permutation (PRP) as a random shuffle, which we also verify in this case. As a result, practical VCs only have computational security and require true random number generators (TRNGs) to achieve information-theoretic security. We derive a rational evaluation standard for VC's security from the detailed attack scenario. Based on it, we consider an attack method using multiple shared images that is more effective than the brute-force search. In this study, we execute computer simulations of XOR differential attack on two shared images and observe the bias on the appearance frequency of the differential output. From these above results, we can conclude that it is necessary to generate the basis matrices with the same Hamming weight for all rows to guarantee the security of VCs.