Gökay Saldamlı, Kavitha Karunakaran, Vidya K. Vijaykumar, Weiyang Pan · 6 authors
Automakers in collaboration with technology industries are swiftly innovating and transforming the automobile industry. The current trend of connected cars relies on retrieving various kinds of vehicular data since there is a huge demand from associated entities included insurance companies, vehicle buyers/sellers and government authorities. Currently, the data collection is done either manual or unsupervised that poses trust, legitimacy and accuracy issues such as duplicated or falsified vehicular data records, tampered safety checks and meddled driving history, etc. Hence, a strong tool that can protect the vehicular data; log the changes for audit purposes and eventually build the trust in the system is necessary. We propose the use of blockchain technology for these needs. The proposed solution involves connecting an IoT module to a car data port to collect rich telemetric data; analyze the driving behaviors on various fronts and storing the outcomes to a blockchain. For various good reasons we use the Ethereum blockchain in this study. However, other blockchain deployments can also be utilized. If adopted by the stakeholders, the proposed solution can provide a trusted, transparent and easily accessible platform to auto buyers/sellers, insurance agencies, vehicle dealers, law enforcements and vehicle history providers.
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Pranshu Bansal, Rohit Panchal, Sarthak Bassi, Amit Kumar
Blockchain is a decentralized ledger used to securely exchange digital currency, perform deals and transactions. Each member of the network has access to the latest copy of encrypted ledger so that they can validate a new transaction. Various features such as decentralization, trustworthiness, trackability and immutability are provided by the blockchain technology. This paper provides the blockchain architecture and explains the concept, characteristics, need of Blockchain in Security, how Bitcoin works and to enhance the security in the field of IoT. It attempts to highlights the role of Blockchain in shaping the future of Cyber Security, Cryptocurrency and adoption of IoT. This paper explains the need of blockchain technology in various technical fields and shows various advantages over conventional system.
In the dawn of crypto-currencies the most talked currency is Bitcoin. Bitcoin is widely flourished digital currency and an exchange trading commodity implementing peer-to-peer payment network. No central athourity exists in Bitcoin. The users in network or pool of bitcoin need not to use real names, rather they use pseudo names for managing and verifying transactions. Due to the use of pseudo names bitcoin is apprehended to provide anonymity. However, the most transparent payment network is what bitcoin is. Here all the transactions are publicly open. To furnish wholeness and put a stop to double-spending, Blockchain is used, which actually works as a ledger for management of Bitcoins. Blockchain can be misused to monitor flow of bitcoins among multiple transactions. When data from external sources is amalgamated with insinuation acquired from the Blockchain, it may result to reveal user's identity and profile. In this way the activity of user may be traced to an extent to fraud that user. Along with the popularity of Bitcoins the number of adversarial attacks has also gain pace. All these activities are meant to exploit anonymity and privacy in Bitcoin. These acivities result in loss of bitcoins and unlawful profit to attackers. Here in this paper we tried to present analysis of major attacks such as malicious attack, greater than 52% attacks and block withholding attack. Also this paper aims to present analysis and improvements in Bitcoin's anonymity and privacy.
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Gökay Saldamlı, Vamshi Reddy, Krishna S. Bojja, Manjunatha K. Gururaja · 6 authors
The health care industry is one of the important service providers that improves people lives. As the cost of the healthcare service increases, health insurance becomes the only way to get quality service in case of an accident or a major illness. As health insurance will reduces the costs and provides financial and economic stability for an individual. One of the main tasks of healthcare insurance providers is to monitor and manage the data and to provide support to customers. Due to regulations and business secrecy, insurance companies do not share the patient's data but since the data are not integrated and not in sync between insurance providers, there has been an increase in the number of fraud's occurring in healthcare. Often times ambiguous or false information is provided to health insurance companies in order to make them pay for some false claims to the policy holders. The individual policyholder may also claim benefits from multiple insurance providers. There is a financial loss of billions of dollars each year as estimated by the National Health Care Anti-Fraud Association (NHCAA). In order to prevent health insurance fraud, it is necessary to build a system to securely manage and monitor insurance activities by integrating data from all the insurance companies. As blockchain provides an immutable data maintaining and sharing, we propose a blockchain based solution for health insurance fraud detection.
Blokzincir, bitcoin dijital para biriminin de alt yapısını oluşturan yeni bir teknolojidir. Popüler ve yaygın yatırımlar sayesinde günlük gerçekleştirilen bitcoin işlem sayısı gün geçtikçe artmaktadır. Bitcoin verisi her geçen gün artmakta ve dolayısıyla artan büyük bitcoin verisinin analizi ve madenciliği için yeni veri madenciliği yöntemlerine ihtiyaç duyulmaktadır. Buna ek olarak, Bitcoin fiyatındaki dalgalanmalar ve anormal fiyat değişimleri ve bu değişimlerdeki anormalliklerin keşfi ekonomistler için büyük önem taşımaktadır. Bu çalışmada, 2012-2019 yıllarına ait 8 yıllık bitcoin fiyat veri kümesi kullanılarak bitcoin fiyat farkı ve bitcoin fiyatı yüzdesel farkı olmak üzere iki farklı veri kümesi oluşturulup, anormallik tespiti gerçekleştirilmiştir. Öncelikle veri kümesi ön işlem aşamasından geçirilerek gereksiz sütunlar çıkarılmıştır ve daha sonra günlük fiyat farkları kullanılarak veri setleri oluşturulup, DBSCAN algoritması ile anormallik tespiti yapılmıştır. Ayrıca bu çalışmada DBSCAN aloritmasının sonuçları istatistiksel yöntemin sonuçları ile karşılaştırılıp, tartışılmıştır. Sonuçlar incelendiğinde, DBSCAN algoritması ve istatistiksel metodun bitcoin fiyatlarındaki anormallikleri her iki veri kümesinde de başarıyla tespit edebildiği görülmüştür. Bununla birlikte DBSCAN algoritması normal günlük fiyat değişimlerine yakın olan anormak fiyat değişimlerini de keşfedebildiği için istatistiksel metottan daha iyi performans göstermiştir. Ayrıca, bu çalışmada bitcoin fiyat farkı veri kümesi ve bitcoin fiyatı yüzdesel farlı veri kümesi karşılaştırılmış ve her bir veri kümesi için olan sonuçlar ve sebepleri tartışılmıştır. NOT: Makalenin düzeltilmiş haline Alper Ecemis - Düzeltme ulaşabilirsiniz.
Bitcoin en popüler ve yaygın olarak kullanılan dijital para birimidir. Bu nedenle, Bitcoin fiyat hareketinin tahmini finansal piyasalar için büyük önem taşımaktadır. Bitcoin fiyat tahmininde ekonometrik modellerin yanında veri madenciliği yöntemlerinden de faydalanılmaktadır. Veri madenciliğinde kullanılan araç ve yöntemler yardımıyla veriler modellenerek yararlanılacak bilgilere dönüştürülürler. K-Star algoritması veri madenciliği, obje tanımlama ve kontrol sistemleri gibi birçok alanda kullanılmakta olan örnek tabanlı bir yaklaşımdır. Bu çalışmada Makroekonomik değişkenlerin Bitcoin fiyatlarını etkileme seviyeleri, Makine Öğrenme yöntemlerinden Lazy Learning Öğrenmeye Dayalı K-Star Algoritması kullanılarak analiz edilmiştir. Çalışmanın veri seti, bağımlı ve bağımsız değişkenlerin 3 Ocak 2017 - 30 Ocak 2019 yılları arasındaki iş günü bazında 510 adet gözlem değerini içermektedir. Bu gözlemlerin 474 adedi (%93’ü) algoritmanın modellenmesi (eğitim) için, 36 adedi (%7’si) ise sınıflandırma (test) için kullanılmıştır. Modelin Bitcoin fiyatlarını gelecek dönem “yükseliş” mi yoksa “düşüş” mü göstereceğine ilişkin sınıflandırma başarısının %61,1 oranında olduğu, Bitcoin fiyatlarının “yükseliş” göstereceğine ilişkin doğru sınıflandırma başarısının %71,42, “düşüş” göstereceğine ilişkin doğru sınıflandırma başarısının ise %46,66 olduğu tespit edilmiştir. Sonuç olarak Makine Öğrenme Tekniğinin belli bir performans gösterdiği ancak Bitcoin fiyatlarının öngörülebilirliğinin henüz beklentinin altında olduğu ortaya çıkmıştır.
This research was conducted to analyze cryptocurrency volatility. Gold, Dollar Index, and Composite Stock Prices Index in the Indonesia Stock Exchange (IDX) variable are used as independent variables. The cryptocurrency objects in this study are Bitcoin and Ethereum which have the largest market capitalization. The data used in this study is from 1st January 2017 to 31st December 2019. This study uses GARCH analysis. The result of this study indicates that the volatility of Bitcoin and Ethereum is not influenced by other variables, but it is influenced by the prices of each Bitcoin and Ethereum at past prices. This shows that the cryptocurrency market is an inefficient market.
The video created by a surveillance cameras plays a crucial role in crime prevention and examinations in smart cities. The closed-circuit television camera (CCTV) is essential for a range of public uses in a smart city; combined with Internet of Things (IoT) technologies they can turn into smart sensors that help to ensure safety and security. However, the authenticity of the camera itself raises issues of building up integrity and suitability of data. In this paper, we present a blockchain-based system to guarantee the trustworthiness of the stored recordings, allowing authorities to validate whether or not a video has been altered. It helps to discriminate fake videos from original ones and to make sure that surveillance cameras are authentic. Since the distributed ledger of the blockchain records the metadata of the CCTV video as well, it is obstructing the chance of forgery of the data. This immutable ledger diminishes the risk of copyright encroachment for law enforcement agencies and clients users by securing possession and identity.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Privacy and Trust are critical issues in automation systems/ transportation systems. Today’s Vehicle is need of everyone for moving one place to another. Together this, data security plays an important role in automation systems as critical user’s (vehicle’s user) data is moved to another user though internet with the help of wireless devices and routes which includes optical fiber, radio channels, etc. In fact, each and every device is connected to the internet and is linked to each other, thus forming the Internet of Things (IoT). As the network is moving towards wireless applications, many threats to vehicles (autonomous vehicles) are becoming a critical problem for vehicles users and service providers. A majority of these attacks can be spotted and detected with the hep of a number of intrusion detection techniques which were elucidated in the earlier decade. These techniques are highly efficient in the identification of any form of individual breaches into the system by catching hold of invalid data access. A few of the systems which need safety are an integral part of Wireless Networks which consist of WLANs (Wireless Local Area Networks), WPANs (Wireless Personal Area Networks), etc. WPAN family further constitutes of three networks which are WSNs (Wireless Sensor Networks), mobile phones and RFID (Radio Frequency Identification like On Board Units (OBUs)). Since digitization is taking place in each and every sector, i.e., defence, healthcare, education, automation industries etc., and so the threat to data also exist. In this article, we protect IoT based environment based smart/ Intelligent Transportation Systems (ITS) using a novel concept “Blockchain Technology”. With proposing novel solution called ‘’PChain using Blockchain Technology (BT), we received many benefit in ITS’s applications. We discuss several open issues and challenges for the respective technology in near future (or next decade).
In this day and age, agricultural producers are faced with multiple obstacles, from seasonal changes to the broken supply chain; their occupation is very laborious and demanding. In such a situation, a distinct information database consisting of credible information would be very helpful. Transfer of knowledge is important in all aspects of this occupation, may it be about market trends or about profitable practices. Third-party interference in this aspect may cause the spread of misinformation. This can be curbed by using blockchain, a data ledger which is reliable and incorruptible. Here an attempt to analyse the different ways in which blockchain technology can be incorporated in the agricultural supply chain, as a transparent and dependable transaction mechanism is explored.
In recent years, Bitcoin is rising and become an attractive investment for traders. Unlike stocks or foreign exchange, Bitcoin price is fluctuated, mainly because of its 24-hours a day trading time without close time. To minimize the risk involved and maximize capital gain, traders and investors need a way to predict the Bitcoin price trend accurately. However, many previous works on cryptocurrency price prediction forecast short-term Bitcoin price, have low accuracy and have not been cross-validatedThis paper describes the baseline neural network models to predict the short-term and the long-term Bitcoin price change. Our baseline models are the Multilayer Perceptron (MLP) and the Recurrent Neural Networks (RNN) models. Data used are Bitcoin's blockchain from August 2010 until October 2017 with 2-days period and the total amount of 1300 data. The models generated are predicting both for short-term and long-term price change, from 2-days until 60-days.The result shows that long-term prediction has a better result than short-term prediction, with the best accuracy in Multilayer Perceptron when predicting the next 60-days price change and Recurrent Neural Networks when predicting the next 56-days price change. Multilayer Perceptron outperforms Recurrent Neural Networks with accuracy of 81.3 percent, precision 81 percent, and recall 94.7 percent.
Blockchain and cryptocurrencies have risen to popularity in the recent years to a great extent due to its increasing trading volumes and huge capitalization in the market. These cryptocurrencies are being used not only for trading but are being accepted for monetary transactions as well these days. As the prices fluctuate and return on investment increases investors, traders and general public are showing increased interest towards bitcoin and altcoins. This research focuses on implementing forecasting models that will return accurate price predictions for cryptocurrencies. Prices for Bitcoin, Ethereum and Litecoin are predicted using the traditional forecasting model for timeseries ARIMA, the Prophet Model and deep learning algorithm LSTM. The results of the three models were evaluated and the LSTM Model was found to outperform the Prophet as well as the ARIMA model.
Jan 1, 2020·Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
In recent years, Bitcoin has attracted a lot of attention because of its nature that supports encryption technology and monetary units. For traders, Bitcoin becomes a promising investment since its fluctuating prices potentially draw high profit (the higher the risk the higher the return). Unlike co
The use of computationally intensive systems that employ machine learning algorithms is increasingly common in the field of finance. New state of the art deep learning architectures for time series forecasting are being developed each year making them more accurate than ever. This study evaluates the predictive power of the N-BEATS deep learning architecture trained on Bitcoin daily, hourly, and up-to-the-minute data in comparison with other popular time series forecasting methods such as LSTM and ARIMA. Prediction errors are measured with Mean Average Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results suggest that the developed N-BEATS model has promising predictive power compared to LSTM and ARIMA models.
Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.
In agricultural production, the supply chain transparency and data integration system plays a major role in certifying food products genuinely. Anyway, agriculturist like (sodbuster, granger, husbandman, etc.) right now are facing n number of problems like relationship between supplier and retailer, transparency causes devastating in production, less financial foundations for agriculturist in developing countries, overpriced interceder and hence it is very difficult to maintain the records and a state of satisfaction with a centralized approach. To address the problems arising from the farmers related to agriculture, the blockchain technology plays a major role in the agriculture industry by improving transparency and food provenance in the supply chain, which is featured by the distributed ledger, centralized servers, P2P (Peer to Peer) networks, As in [1] [10] RFID (Radio-Frequency Identification) tag, consensus verification. Hence, the proposed work explores the different problems faced in agriculture production and the solutions to those problems are addressed by using blockchain technology. By applying blockchain technology as in [5] [7] [8] [9] the supply chain and food provenance not only lengthens the practical domain of blockchain but also upkeeps real heroes of the country around agricultural production. The theoretical analysis of the proposed model reveals that the use of blockchain technology in the food tracking system builds trust among different stakeholders at different stages in the process of agriculture production and provides a hundred percent benefit to them.
Adnan Iftekhar, Xiaohui Cui, Mir Hassan, Wasif Afzal
Food supply chain plays a vital role in human health and food prices. Food supply chain inefficiencies in terms of unfair competition and lack of regulations directly affect the quality of human life and increase food safety risks. This work merges Hyperledger Fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity unique for each food package while keeping it uncomplicated. It keeps the records of business transactions that are secured and accessible to stakeholders according to the agreed set of policies and rules without involving any centralized authority. This paper focuses on exploring and building an uncomplicated, low-cost solution to quickly link the existing food industry at different geographical locations in a chain to track and trace the food in the market.