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.

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。

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.

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.

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.

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.

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.

Novel traveling quantum anonymous voting scheme via GHZ states

Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)states.In this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting content.The voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting results.To improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also presented.Compared with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and non-repeatability.Furthermore,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

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%).

基于Voting和Stacking集成算法的岩爆倾向性预测

岩爆是矿山深部开采中常见的地质灾害,准确预测岩爆可降低矿山生产安全风险。将机器学习应用于岩爆预测是切实可行的,但仅用一种方法或将几种方法简单结合对于提高岩爆预测的准确性或泛化性作用十分有限。基于此,将最大切向应力、应力集中系数、脆性系数、弹性能量指数等作为指标,利用231组有效岩爆数据,基于Voting和Stacking集成算法,融合精确率较高的6种基础分类器(LR、RF、SVM、DT、KNN、GNB),建立了4个集成分类器V 1、V 2、S 1和S 2。根据预测结果的混淆矩阵计算了精确率、准确率、召回率及F 1分数,对各分类器性能进行了评估,结果表明:基础分类器中SVM、RF对Ⅰ级、Ⅱ级样本较敏感,KNN对Ⅲ级、Ⅳ级样本更敏感;RF、SVM整体预测效果最好,精确率分别为0.93、0.94;集成分类器相对于基础分类器性能均有不同程度的提升,但受性能较差的基础分类器及投票机制影响,Voting集成分类器整体性能弱于Stacking集成分类器;4个集成分类器中S 1性能提升最显著,预测效果最佳,精确率、准确率、召回率、F 1分数分别为0.95、0.97、0.96、0.95;将基于Stacking算法构建的集成分类器S 1应用于秦岭隧道的岩爆预测,预测结果与工程现场实际一致,验证了其可靠性。

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.

基于集成算法的在线购物平台消费者评价情感分析与研究

文章对在线购物平台的消费者评价数据进行了情感分析和分类。通过使用Python实现自动化浏览器驱动和反爬虫技术,成功采集了某东购物平台的消费者评价信息。文章提出了一种改进的集成算法,将LSTM、BiGRU、BiLSTM作为分类器,分别采用Voting和Bagging方法进行集成。结果表明,与传统的贝叶斯和逻辑回归相比,LSTM+Bagging集成算法在准确率方面分别提高了5.9%和6%,而与LSTM+Voting集成算法相比,准确率提高了0.5个百分点。另外,LSTM+Bagging模型在稳定性和鲁棒性方面表现优于LSTM+Voting算法。

基于SVM-DT-MLP模型的Web日志异常流量检测研究

随着Web应用程序的普及,网络攻击和安全漏洞的风险日益增加。Web日志文件详细记录了网站运行信息,对日志中的流量进行分类从而检测出异常攻击流量是保障网页长期提供稳定、安全服务行之有效的方法之一。文中将Voting特征选择与Stacking集成相结合,构建了SVM-DT-MLP模型,并将其用于Web日志异常流量检测。测试结果表明,SVM-DT-MLP模型的性能显著优于单一算法模型,其Precision(精确度)达到92.44%,Recall(召回率)达到92.43%,F1-Score(F1值)达到92.44%。这意味着该模型能够有效地检测出异常攻击流量,并在保障网页提供稳定和安全服务方面具有很好的效果。

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。