Preventing Phishing Attack on Voting System Using Visual Cryptography

Phishing is one of the most common social engineering attacks that users over the internet fall for. An example is voting systems, and because such systems should be accurate and error free, phishing prevention techniques are crucial. Visual Cryptography (VC) is utilized for efficient voting system authentication to cast votes. VC is one of the most secure approaches for privacy protection as it ensures the confidentiality of the voting system. This paper discusses proposed phishing prevention methods and compares different proposed methods.

Voting System Based on Blockchain

Online ballot box system has the advantages of high efficiency and environmental protection,but the existing network voting technology still has a lot of matter.Almost all electronic voting system could be proved to be intrusion.The administrator of the system could tamper with the data for benefit,and the system may be attacked by hackers.The safety and fairness of the existing network voting system depend entirely on the safety and credibility of the website itself,but these cannot guarantee the fairness of voting.Make full use of blockchain technology,so that voting,even if there are malicious participants,but also to ensure the correctness and safety of the vote.The introduction of block chain technology,block chain has decentralized,data tampering and other characteristics.P2P network is applied in the block chain layer to construct a distributed database,digital signature algorithm and encryption technology are used to ensure that the data cannot be tampered with,consensus network algorithm is used to ensure the consistency of the data in the network,and timestamp technology is applied to save the data blocks in a chain structure connected end to end.It paper focuses on the implementation of P2P network networking mode,node block synchronization,data and block verification mechanism and consensus mechanism to ensure data consistency in the network layer of block chain layer.Using time stamp,Merkle tree,asymmetric encryption and other technologies to design data blocks and use chain structure to store data blocks.Combined with the characteristics of blockchain,a fair and transparent voting system is constructed.Model aims to apply the block chain technology to the voting scenario and design a secure block chain voting architecture.It system is designed and developed based on the block chain system.It makes full use of its decentralization,removes the dependence of electronic voting on trusted third parties,and protects the privacy of voters and candidates.Data cannot be tampered with.Once the data are stored in the block chain,it cannot be tampered with.It provides a real and credible database.

Network-Based Voting System—Evaluation and Optimization of CCMVS

This paper gives a brief introduction to a novel voting system, the Network-based Voting System (NVS). The system design is based on the careful analysis and evaluation of a traditional voting system, the computer controlled and managed voting system. The new system integrates technologies such as image processing, networking and databases to enhance three aspects of system performance: data collection, data transfer, and data management. Experiments have proved that the performance of the network-based voting system is superior to the CCMVS.

Anonymous voting for multi-dimensional CV quantum system

We investigate the design of anonymous voting protocols,CV-based binary-valued ballot and CV-based multi-valued ballot with continuous variables（CV） in a multi-dimensional quantum cryptosystem to ensure the security of voting procedure and data privacy.The quantum entangled states are employed in the continuous variable quantum system to carry the voting information and assist information transmission,which takes the advantage of the GHZ-like states in terms of improving the utilization of quantum states by decreasing the number of required quantum states.It provides a potential approach to achieve the efficient quantum anonymous voting with high transmission security,especially in large-scale votes.

Oscillatory Failure Detection for Flight Control System Using Voting and Comparing Monitors

Oscillatory failure cases(OFC)detection in the fly-by-wire(FBW)flight control system for civil aircraft is addressed in this paper.First,OFC is ranked four levels:Handling quality,static load,global structure fatigue and local fatigue,according to their respect impact on aircraft.Second,we present voting and comparing monitors based on un-similarity redundancy commands to detect OFC.Third,the associated performances,the thresholds and the counters of the monitors are calculated by the high fidelity nonlinear aircraft models.Finally,the monitors of OFC are verified by the Iron Bird Platform with real parameters of the flight control system.The results show that our approach can detect OFC rapidly.

基于voting集成的智能电能表故障多分类方法

为提升智能电能表故障准确分类能力,助力维护人员迅速排除故障,提出基于投票法voting集成的智能电能表故障多分类方法。针对实际智能电能表故障数据进行编码预处理,基于皮尔逊系数法筛选智能电能表故障分类关键影响因素,结合合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)算法解决数据类别不平衡问题,由此建立模型所需数据集,再通过投票法进行模型融合,结合粒子群PSO(particle swarm optimization)确定各基模型的权重,据此构建基于极限梯度提升树(extreme gradient boosting trees, XGBT)、K近邻(k-nearest neighbor, KNN)和朴素贝叶斯(naive bayes, NB)模型的智能电能表故障多分类方法。实测实验结果表明:所提出方法能有效实现智能电能表的故障快速准确分类,与现有方法相比,在智能电能表的故障分类精确率、召回率及F1-Score均有明显提升。

New Parallel N-Input Voting for Large Scale Fault-Tolerant Control Systems

Average （mean） voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine（PRAM） structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.

A Novel Fuzzy Membership Partitioning for Improved Voting in Fault Tolerant System

This paper presents a novel technique for improved voting by adaptively varying the membership boundaries of a fuzzy voter to achieve realistic consensus among inputs of redundant modules of a fault tolerant system. We demonstrate that suggested dynamic membership partitioning minimizes the number of occurrences of incorrect outputs of a voter as compared to the fixed membership partitioning voter implementations. Simulation results for the proposed voter for Triple Modular Redundancy (TMR) fault tolerant system indicate that our algorithm shows better safety and availability performance as compared to the existing one. However, our voter design is general and thus it can be potentially useful for improving safety and availability of critical fault tolerant systems.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

基于Tensor Voting的蚁蛉翅脉修补

针对蚁蛉模式识别中蚁蛉翅脉断裂问题,利用Tensor Voting技术修补其数字照片中断裂的翅脉;展示将其应用于蚁蛉模式识别前期处理,以获取主要翅脉尽量完整信息的算法;数值实验中采用3种蚁蛉翅的图像作为测试,收到了很好的结果.

应用Voting Machine构建研究型、互动型的双语物理课堂的研究与实践

高等教育"质量工程"的实施为高等学校本科教学提出了更新、更高的要求和挑战.本文报道了应用Voting Machine这一具有强大的互动和统计功能的教学设备在双语物理课堂上开展研究型、互动型教学的实践和研究成果.

Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.

物理课堂教学评价的一种先进工具———Voting Machine评价系统介绍

本文介绍美国俄亥俄州立大学研制的Voting Machine教学评价系统,详细论述了该教学评价系统的组成和安装、师生使用方法以及系统的理论依据。

Dynamic weighted voting for multiple classifier fusion:a generalized rough set method

To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.

Constrained voting extreme learning machine and its application

Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

Multi-Candidate Voting Model Based on Blockchain

Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.

A Secure Anonymous Internet Electronic Voting Scheme Based on the Polynomial

In this paper, we use the polynomial function and Chaum＇s RSA （Rivest, Shamir, Adleman） blind signature scheme to construct a secure anonymous internet electronic voting scheme. In our scheme, each vote does not need to be revealed in the tallying phase. The ballot number of each candidate gets is counted by computing the degrees of two polynomials＇ greatest common divisor. Our scheme does not require a special voting channel and communication can occur entirely over the current internet.

A random forest algorithm based on similarity measure and dynamic weighted voting

The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.

Improved PBFT protocol based on phase voting and threshold signature

The communication complexity of the practical byzantine fault tolerance(PBFT)protocol is reduced with the threshold signature technique applied to the consensus process by phase voting PBFT(PV-PBFT).As most communication occurs between the primary node and replica nodes in PV-PVFT,consistency verification is accomplished through threshold signatures,multi-PV,and multiple consensus.The view replacement protocol introduces node weights to influence the election of a primary node,reducing the probability of the same node being elected primary multiple times.The experimental results of consensus algorithms show that compared to PBFT,the communication overhead of PV-PBFT decreases by approximately 90% with nearly one-time improvement in the throughput relative and approximately 2/3 consensus latency,lower than that of the scalable hierarchical byzantine fault tolerance.The communication complexity of the PBFT is O(N^(2)),whereas that of PV-PBFT is only O(N),which implies the significant improvement of the operational efficiency of the blockchain system.

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data collected supports the real-time decision-making required for diverse applications.The communication infrastructure relies on different network types,including the Internet.This makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating effects.However,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks.The objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 dataset.However,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the dataset.The proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional reduction.In addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack cate-gories.The proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).