A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition

A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy than single parameter identification method. The facial recognition system uses Viola-Jones algorithm along with rectangular Haar feature selection method for detection and extraction of features to develop a biometric template and for feature extraction during the voting process. Cascaded machine learning based classifiers are used for comparing the features for identity verification using GPCA (Generalized Principle Component Analysis) and K-NN (K-Nearest Neighbor). It is accomplished through comparing the Eigen-vectors of the extracted features with the biometric template pre-stored in the election regulatory body database. The results of the proposed system show that the proposed cascaded design based system performs better than the systems using other classifiers or separate schemes i.e. facial or finger print based schemes. The proposed system will be highly useful for real time applications due to the reason that it has 91% accuracy under nominal light in terms of facial recognition. with bags of paper votes. The central station compiles and publishes the names of winners and losers through television and radio stations. This method is useful only if the whole process is completed in a transparent way. However, there are some drawbacks to this system. These include higher expenses, longer time to complete the voting process, fraudulent practices by the authorities administering elections as well as malpractices by the voters [1]. These challenges result in manipulated election results

RFID SMART ELECTRONIC VOTING MACHINE(EVM)

The Electronic Voting Machine (EVM) is indeed a basic electronic device that replaces the ballot papers and boxes that were previously used in traditional voting systems to record votes. Democracy is founded on the fundamental right to vote, or simply voting in elections. Previously, in all elections, whether state or federal, a voter would stamp his or her preferred candidate's name and then fold the ballot paper according to a set procedure before placing this in the Ballot Box. This is a lengthy, time-consuming process that is prone to mistakes. This condition persisted until computerized voting machines fundamentally transformed the election scene

Eesti elektrooniline ID-kaart ja selle turvaväljakutsed

Eesti elektrooniline isikutunnistust (ID-kaart) on üle 18 aasta pakkunud turvalist elektroonilist identiteeti Eesti kodanikele. Avaliku võtme krüptograafia ja kaardile talletatud privaatvõti võimaldavad ID-kaardi omanikel juurde pääseda e-teenustele, anda juriidilist jõudu omavaid digiallkirju ning elektrooniliselt hääletada. Käesolevas töös uuritakse põhjalikult Eesti ID-kaarti ning sellega seotud turvaväljakutseid. Me kirjeldame Eesti ID-kaarti ja selle ökosüsteemi, seotud osapooli ja protsesse, ID-kaardi elektroonilist baasfunktsionaalsust, seotud tehnilisi ja juriidilisi kontseptsioone ning muid seotud küsimusi. Me tutvustame kõiki kasutatud kiipkaardiplatforme ja nende abil väljastatud isikutunnistuste tüüpe. Iga platformi kohta esitame me detailse analüüsi kasutatava asümmeetrilise krüptograafia funktsionaalsusest ning kirjeldame ja analüüsime ID-kaardi kauguuendamise lahendusi. Lisaks esitame me süstemaatilise uurimuse ID-kaardiga seotud turvaintsidentidest ning muudest sarnastest probleemidest läbi aastate. Me kirjeldame probleemide tehnilist olemust, kasutatud leevendusmeetmeid ning kajastust ajakirjanduses. Käesoleva uurimustöö käigus avastati mitmeid varem teadmata olevaid turvaprobleeme ning teavitati nendest seotud osapooli. Käesolev töö põhineb avalikult kättesaadaval dokumentatsioonil, kogutud ID-kaartide sertifikaatide andmebaasil, ajakirjandusel,otsesuhtlusel seotud osapooltega ning töö autori analüüsil ja eksperimentidel.For more than 18 years, the Estonian electronic identity card (ID card) has provided a secure electronic identity for Estonian residents. The public-key cryptography and private keys stored on the card enable Estonian ID card holders to access e-services, give legally binding digital signatures and even cast an i-vote in national elections. This work provides a comprehensive study on the Estonian ID card and its security challenges. We introduce the Estonian ID card and its ecosystem by describing the involved parties and processes, the core electronic functionality of the ID card, related technical and legal concepts, and the related issues. We describe the ID card smart card chip platforms used over the years and the identity document types that have been issued using these platforms. We present a detailed analysis of the asymmetric cryptography functionality provided by each ID card platform and present a description and security analysis of the ID card remote update solutions that have been provided for each ID card platform. As yet another contribution of this work, we present a systematic study of security incidents and similar issues the Estonian ID card has experienced over the years. We describe the technical nature of the issue, mitigation measures applied and the reflections on the media. In the course of this research, several previously unknown security issues were discovered and reported to the involved parties. The research has been based on publicly available documentation, collection of ID card certificates in circulation, information reflected in media, information from the involved parties, and our own analysis and experiments performed in the field.https://www.ester.ee/record=b541416

The low area probing detector as a countermeasure against invasive attacks

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMicroprobing allows intercepting data from on-chip wires as well as injecting faults into data or control lines. This makes it a commonly used attack technique against security-related semiconductors, such as smart card controllers. We present the low area probing detector (LAPD) as an efficient approach to detect microprobing. It compares delay differences between symmetric lines such as bus lines to detect timing asymmetries introduced by the capacitive load of a probe. Compared with state-of-the-art microprobing countermeasures from industry, such as shields or bus encryption, the area overhead is minimal and no delays are introduced; in contrast to probing detection schemes from academia, such as the probe attempt detector, no analog circuitry is needed. We show the Monte Carlo simulation results of mismatch variations as well as process, voltage, and temperature corners on a 65-nm technology and present a simple reliability optimization. Eventually, we show that the detection of state-of-the-art commercial microprobes is possible even under extreme conditions and the margin with respect to false positives is sufficient.Peer ReviewedPostprint (author's final draft

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

Internet voting in Estonia 2005–2019: Evidence from eleven elections

Internet voting is a highly contested topic in electoral studies. This article examines Internet voting in Estonia over 15 years and 11 nation-wide elections. It focuses on the following questions: How is Internet voting organized and used in Estonia? How have the Estonian Internet voting system and its usage evolved over time? What are the preconditions and consequences of large-scale deployment of Internet voting? The results suggest that the rapid uptake and burgeoning usage rates reflect the system's embeddedness in a highly developed digital state and society. Through continuous technological and legal innovation and development, Estonia has built an advanced Internet voting system that complies with normative standards for democratic elections and is widely trusted and used by the voters. Internet voting has not boosted turnout in a setting where voting was already easily accessible. Neither has it created digital divides: Internet voting in Estonia has diffused to the extent that socio-demographic characteristics no longer predict usage. This, combined with massive uptake, reduces incentives for political parties to politicize the novel voting mode

Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers

Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets as “OFF” or “ON” based on their normal routine in homes. We have integrated emerging blockchain technology to provide secure, decentralized, and trustworthy authentication and recognition of IoT devices. Our system consists of a 5V relay circuit, various sensors, and a Raspberry Pi server and database for managing devices. We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server. The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings. It is essential to use inexpensive, scalable, and readily available components and technologies in smart home automation systems. Additionally, we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments, such as cyberattacks, hardware security, and other cyber threats. The trial results support the proposed system and demonstrate its potential for use in everyday life