Blockchain technology has revolutionized secure and decentralized digital transactions. However, the emergence of quantum computing presents a significant threat to traditional cryptographic protocols, particularly public-key encryption mechanisms such as RSA and Elliptic Curve Cryptography (ECC). Quantum computers, leveraging Shor’s and Grover’s algorithms, can efficiently break these encryption schemes, compromising blockchain security. This paper explores quantum-secure blockchain protocols that integrate post-quantum cryptographic (PQC) techniques such as lattice-based, hash-based, and code-based cryptography to resist quantum attacks. Additionally, we evaluate quantum-resistant consensus mechanisms like Quantum-Secure Proof of Stake (QS-PoS) and Quantum-Protected Byzantine Fault Tolerance (Q-BFT). Through simulation-based performance analysis, we demonstrate that quantum-safe blockchain models can achieve robust security while maintaining efficient transaction processing. Our findings suggest that a hybrid approach, combining classical cryptographic elements with post-quantum algorithms, provides the best balance between security, performance, and scalability.
Proof-of-Context (PoC) protocols aim to ensure fairness and integrity in smart contract execution by cryptographically binding on-chain transactions to verifiable off-chain contextual data. Traditional consensus mechanisms (e.g., Proof-of-Work, Proof-of-Stake) focus on ordering and validation of transactions but do not address whether the contextual conditions that should govern contract execution are satisfied. In this manuscript, we propose a novel PoC framework that leverages decentralized oracles, zero-knowledge proofs, and time-stamped Merkle commitments to provide verifiable evidence that all pre-specified preconditions and environmental parameters were met at execution time. We detail the design of the protocol, implement a prototype on an Ethereum testnet using Chainlink oracles and zk-SNARKs, and conduct a performance evaluation under varying network and workload conditions. Our results show that PoC incurs a modest overhead—on average 5% additional gas cost and 200 ms added latency per proof generation—while dramatically enhancing auditability and reducing the risk of context-based manipulation or dispute. We conclude that PoC protocols offer a practical mechanism for enforcing fairness in a wide range of decentralized applications, from DeFi loans conditioned on real-world data to NFT minting events gated by dynamic criteria. Finally, we discuss the scope, limitations, and future research directions for broader deployment.
As machine learning spreads into fields of use that demand secure and private authentication, ensuring such authentication is becoming increasingly critical.Zero Knowledge Proofs (ZKPs) have been presented as a cryptographic technique of transforming authentication without data leakage [1].In this research, the use of ZKPs in the AI authentication frameworks is looking into privacy, security and scalability.The model predictions are verified by the proposed system using advanced ZKP protocols like zkSNARKs and zkSTARKs without revealing model parameters or user inputs [3].Our system is able to reach better computational efficiency and lower computation overhead through incorporation of Mystique conversion protocols [7] and fast ZK inference protocols such as ezDPS [6].Results of experiments [5] show that frameworks with ZKP integrated authentication perform better than the standard encryption with respect to both security and performance in decentralized machine learning regimes.Moreover, the solution facilitates verifiability in Federated Learning by integrating blockchain, which helps to increase transparency and trust [4].To overcome the data leakage issue, ZKPs are explored for use in decentralized AI frameworks where secure model deployment is required to generate personalized advice [4].As this research shows, ZKPs offer transformative properties which can be used for authentication in AI systemssuch as in healthcare, finance or IoT network and thus increase the trust in AI driven solutions.
Analisis sentimen terhadap cryptocurrency telah menjadi topik penting dalam riset dan pengembangan di bidang keuangan dan teknologi informasi. Twitter, sebagai platform media sosial yang populer, menjadi sumber data yang berharga untuk memahami sentimen pengguna terhadap cryptocurrency. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap cryptocurrency berdasarkan data dari Twitter menggunakan algoritma Naive Bayes dan Decision Tree. Metode yang digunakan melibatkan pengumpulan data dari Twitter yang berisi percakapan terkait cryptocurrency. Data tersebut kemudian dibersihkan, diproses, dan dianalisis menggunakan algoritma Naive Bayes dan Decision Tree. Naive Bayes digunakan untuk mengklasifikasikan sentimen menjadi positif, negatif, atau netral berdasarkan fitur-fitur teks dari tweet. Sementara itu, Decision Tree digunakan untuk membangun model prediktif yang dapat mengidentifikasi pola sentiment terhadap cryptocurrency..Hasil penelitian menunjukkan bahwa akurasi Naïve Bayes mencapai 80.222%, sedangkan Decision Tree mencapai 65.03%. Dari hasil ini, dapat disimpulkan bahwa Naïve Bayes lebih baik dalam mengklasifikasikan text mining dengan akurasi tertinggi. Perbandingan antara kedua metode menunjukkan perbedaan akurasi yang tidak signifikan, yaitu untuk Naïve Bayes dengan akurasi 80.22%, Presisi 96.90%, dan Recall 62.54%, serta Decision Tree dengan akurasi 65.03%, Presisi 52.02%, dan Recall 98.94%. Analisis opini publik terhadap cryptocurrency mengungkapkan bahwa masyarakat Indonesia cenderung memberikan tanggapan positif terhadap mata uang digital ini setelah dilakukan penelitian ini.
Teknologi blockchain telah menjadi fokus utama dalam pengembangan aplikasi web terdesentralisasi, membuka era baru dalam evolusi internet yang dikenal sebagai Web3. Di tengah perkembangan ini, perusahaan startup PT. Pintar Media Teknologi memperkenalkan aplikasi launchpad cryptocurrency yang mengandalkan teknologi blockchain untuk memberikan solusi terdesentralisasi dalam perdagangan aset crypto. Penulis mendapatkan kesempatan untuk melakukan penelitian pada PT. Pintar Media Teknologi untuk membangun aplikasi launchpad cryptocurrency. Metodologi pengembangan aplikasi blockchain yang diterapkan adalah metodologi Agile Scrum. Melalui analisis fitur-fitur yang dikembangkan, seperti perancangan Entity Relationship Diagram, desain basis data, hingga implementasi fitur-fitur utama seperti autentikasi menggunakan Web3, manajemen token, dan manajemen launchpad. Hasil penelitian ini memberikan wawasan tentang praktik pengembangan aplikasi blockchain dan kontribusi penulis pada penelitian ini adalah merancang dan mengimplementasi sistem informasi launchpad cryptocurrency sesuai dengan kebutuhan para stakeholders sehingga dapat mempercepat proses pengembangan produk.
The feature of Blockchain as distributed ledger that are shared among nodes within a computer network, renowned for its pivotal role in cryptocurrency systems by ensuring a secure and decentralized the role of transaction record which is ensured for maintenance of security and decentralization in cryptocurrency systems.The Linux Foundation host the open-source framework of private blockchain, the Hyperledger Fabric (HLF).Smart contracts are utilized for transaction management and a modular architecture of blockchain framework, providing a foundation for the development of blockchain-based applications through plug-and-play components.In the realm of distributed systems, scalability emerges as a crucial design goal for developers.The most appropriate blockchain platform for the operations of the business industry, which need for the seamless addition of more users and resources without perceptible performance loss.An assessment of scalability is required as a large number of nodes involvement in the implementation of blockchain frameworks.In this paper, the impact of system configurations such as, transaction volume, node types is focused in the transition of V2.2.4 with the various significant issues with the architecture.The throughput, latency, processor, and memory usages are mainly analyzed based on the different number of transactions.According to the performance results of the proposed system, the scalability of the possible number of transactions and the different peer nodes can be supported in the implementation of blockchain-based system for HLF blockchain.
ABSTRAK Penelitian ini berfokus pada masalah keamanan jaringan yang menjadi krusial bagi perusahaan teknologi blockchain dan Non-Fungible Token (NFT) yang rentan terhadap serangan siber seperti DDoS, injeksi SQL, dan malware. Serangan ini tidak hanya menyebabkan kerugian finansial tetapi juga merusak reputasi dan kepercayaan pengguna. Suricata, sebagai sistem deteksi dan pencegahan intrusi open-source, menawarkan berbagai fitur untuk memonitor dan menganalisis lalu lintas jaringan secara real-time. Penelitian ini mengevaluasi efektivitas Suricata dalam mendeteksi ancaman pada web server NFT melalui pendekatan eksperimental. Pengujian dilakukan dengan metode scanning port, web penetration testing, DDoS, dan identifikasi kerentanan sistem web server menggunakan alat seperti NMap, Hping3, Nikto, dan Metasploit. Hasil menunjukkan bahwa Suricata mampu mencatat aktivitas mencurigakan dan mencegah anomali dengan integrasi firewall PFsense. Implementasi Suricata memberikan informasi deteksi serangan web scanning, meskipun tidak memiliki aturan shared object seperti perangkat lunak intrusi lainnya. Penelitian ini memberikan rekomendasi bagi pengembang dan operator platform NFT untuk melindungi aset digital mereka dari serangan siber, serta berkontribusi pada peningkatan keamanan jaringan di sektor NFT. ABSTRACT This research focuses on the critical issue of network security for blockchain technology and Non-Fungible Token (NFT) companies, which are vulnerable to cyberattacks such as DDoS, SQL injection, and malware. These attacks not only cause financial losses but also damage reputation and user trust. Suricata, an open-source intrusion detection and prevention system, offers various features to monitor and analyze network traffic in real-time. This study evaluates the effectiveness of Suricata in detecting threats on NFT web servers through an experimental approach. Testing methods include port scanning, web penetration testing, DDoS, and identifying web server vulnerabilities using tools such as NMap, Hping3, Nikto, and Metasploit. The results show that Suricata can log suspicious activities and prevent anomalies when integrated with the PFsense firewall. While Suricata provides information on web scanning attacks, it lacks shared object rules found in other intrusion software. This research offers recommendations for NFT platform developers and operators to protect their digital assets from cyberattacks and contributes to improving network security in the NFT sector. Thus, this study is highly relevant in the digital era, where information and data security are top priorities for business continuity and user privacy protection.
Ni Ketut Novia Nilasari, Made Sudarma, Nyoman Gunantara
Semakin pesatnya perkembangan teknologi saat ini, dapat memudahkan seluruh kegiatan manusia, sehingga mengakibatkan seluruh aspek tidak bisa lepas dari teknologi tanpa terkecuali bidang keuangan. Dengan berkembangnya teknologi diiringi juga dengan dikenalnya berbagai instrument investasi. Setiap melaksanakan investasi tentu akan selalu ada berbagai resiko yang menyertainya termasuk investasi cryptocurrency salah satunya bitcoin. Tidak seperti mata uang konvensional, bitcoin bersifat tidak desentralisasi sehingga perkembangan harganya tidak dalam pengawasan atau kontrol pihak manapun, dimana jika uang konvensional ada lembaga tertentu yang mengawasi dan mengontrol pergerakannya. Hal tersebut mengakibatkan harga nilai tukar dari bitcoin menjadi tidak konsisten atau tidak stabil. Dengan terdapatnya metode prediksi, pengguna bitcoin bisa menetapkan waktu yang pas untuk menjalankan transaksi. Penelitian ini memiliki tujuan guna memprediksi harga bitcoin dengan menggunakan metode LSTM serta Bi-LSTM. Berdasarkan hasil penelitian diperoleh hasil prediksi terbaik menggunakan metode Bi-LSTM dengan RMSE 1482.73 sedangkan dengan LSTM menghasilkan RMSE sebesar 1768.69 sehingga dapat disimpulkan dari sisi akurasi Bi-LSTM memberikan hasil yang lebih akurat hanya saja dengan Bi-LSTM membutuhkan resourse yang lebih banyak.
The implementation of blockchain technology alongside Artificial Intelligence features that strengthen Internet of Things cloud-based systems through extended data protection, enhanced robotic trust, and decentralized intelligence capabilities.Both potential benefits and obstacles of building blockchain-empowered collaborative AI systems that perform secure computations across multiple parties and present architectural guidelines for privacy protection.Digital transformation now drives various industries forward because of the power combination between IoT and distributed ledger technology and their alignment with AI and edge-fog-cloud computing environments.Blockchain integration with IoT networks protects data integrity by remedying vital privacy and security problems, which creates a robust system that handles decentralized, secure data management.Blockchain technology makes financial operations secure and faster across all payment transactions, trade finance, and asset management operations to build complete trust with banking institutions.Through their mutual partnership, blockchain and robotic technologies develop advanced robotic systems that exhibit better operational performance and use strengthened security systems to address blockchain weaknesses.This leads to better dependability of AIdriven service operations.Multiple forces drive blockchain integration with AI applications because users need stronger data security basics to protect confidential data from unauthorized use or tampering, and they want more reliable robot decision authentication.
The rise of Web 3.0, which is based on independent technologies like blockchain and smart contracts, marks a big change in the financial technology field. This research looks at all the different ways that Web 3.0 can be used in FinTech by looking at real-life examples from Ethereum, Betterment, Wealthfront, DeversiFi, Synthetix, Kyber Network, and Curve Finance. By combining ideas from McKinsey's research, the study shows that Web3 lending sites are growing quickly. In 2021 alone, they gave out over $200 billion in loans. The study shows how Ethereum can be used for smart contracts, how Betterment and Wealthfront's robo-advisory services use AI and machine learning, how DeversiFi's decentralized exchange handles privacy issues, how Synthetix creates on-chain digital assets, how Kyber Network's blockchain-based liquidity protocol is put into use, and how Curve Finance's decentralized platform handles stablecoin transactions. Although there has been success, integrating these technologies is still very hard. The main problems are unclear regulations and technical issues with security, scalability, and interoperability. The final success of Web 3.0 in FinTech will depend on how well these problems are solved, which will help find a good balance between fast technological progress and strong risk management.
The International Journal of Computer Engineering in Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
The International Journal of Computer Science Engineering and Its Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Science Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
Muhammad Sahi, Muhammad Faisal, Yunifa Miftachul Arif, Cahyo Crysdian
Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
\n\t\n\t\t\n\t\t\t\n\t\t\tWith the rapid development of technology and the large amount of digitization in various fields of human life, it is necessary to pay attention to the security and certainty of privacy so that there is no leakage of confidential data, both in the private and governmental domains, especially in Indonesia. In this case cyber-attacks will grow and become more numerous; therefore we need a security principle that can prevent these cyber-attacks, especially in sending something that is sensitive which can be called cryptography. One of the applications that can be implemented regarding this cryptography is the Whatsapp application. WhatsApp claims that the application is safe from data theft and messages being intercepted. However, this is doubtful with the presence of Whatsapp Mod which offers more features than the official application. The security of the modified Whatsapp is questionable, so in this study a test was carried out using the MobSF Framework to find out whether there were security holes that could endanger its users. The results of this research are in the form of a report issued by MobSF regarding the level of danger of the Whatsapp Mod Application. With this research, it is hoped that it will be able to make Whatsapp Mod users aware of the dangers of modified applications and Whatsapp can provide advice and strict action against the Whatsapp Mod developers.\n\t\t\t\n\t\t\n\t\n
Cryptocurrency adalah mata uang digital terdesentralisasi yang diatur oleh pemerintah pusat. Karena cryptocurrency sangat fluktuatif, analisis diperlukan sebelum menggunakan cryptocurrency untuk meminimalkan kerugian. Penelitian ini melakukan perbandingan antara model Long Short Term Memory (LSTM) dan algoritma optimasi seperti Adam dan Root Mean Square Propagation (RMSProp) untuk melakukan prediksi terhadap nilai cryptocurrency. Metode LSTM dioptimasi menggunakan Adam Optimizer dan dievaluasi berdasarkan Root Mean Square Error (RMSE). Dengan demikian diperoleh prediksi nilai RMSE sebesar 0.08217562639465784 yang merupakan nilai error yang kecil sehingga mendekati nilai aktual. Sedangkan nilai RMSE 0.10699215580552895 menggunakan RMSProp mendapatkan nilai yang lebih besar yang berdampak terhadap akurasi hasil prediksi. Dengan demikian kombinasi antara algoritma LSTM dan Adam dapat melakukan prediksi dan mengoptimasi data dengan akurat.
Cryptocurrency merupakan mata uang digital yang dapat digunakan untuk transaksi atau investasi. Investasi aset cryptocurrency saat ini semakin banyak diminati oleh masyarakat. Investasi ini memiliki resiko yang tinggi dikarenakan harganya dapat turun ataupun naik dalam waktu yang singkat. Karena keadaan naik turunnya harga cryptocurrency yang begitu drastis inilah membuat para investor yang berharap ingin mendapatkan keuntungan justru mengalami kerugian. Oleh karena itu, diperlukan sebuah sistem prediksi yang dapat membantu memberikan pertimbangan kepada investor dalam pembelian aset cryptocurrency. Pada penelitian ini menggunakan metode GRU untuk memprediksi harga cryptocurrency, yaitu bitcoin dan ethereum dari tahun 2018 sampai 2021. Data dilakukan pelatihan menggunakan varian nilai window size untuk mendapatkan model dengan window size yang optimal dari nilai error terkecil dengan perhitungan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil pengujian, dengan menggunakan nilai window size sebanyak 2, sistem mendapatkan hasil error yang paling kecil. Perhitungan akurasi prediksi untuk 1, 6, dan 12 bulan berikutnya pada data uji bitcoin masing-masing sebesar 90.26%, 77.74%, dan 75.98%, sedangkan pada data uji ethereum masing-masing sebesar 90.15%, 76,88%, dan 66.09%. Dapat dikategorikan sistem prediksi harga cryptocurrency ini tergolong sangat baik untuk memprediksi 1 bulan berikutnya dan dikategorikan cukup untuk memprediksi 6 dan 12 bulan berikutnya.
The International Journal of Computer Engineering in Research Trends (IJCERT) is a peer-reviewed, open access journal that publishes high-quality research papers, reviews, short communications, and notes in the field of computer science engineering and its research trends. The journal covers a wide range of topics in computer science and engineering, including: Welcome to the International Journal of Computer Engineering in Research Trends (IJCERT), is a peer-reviewed, open access journal dedicated to publishing innovative research papers, reviews, short communications, and notes in the field of computer science engineering and related disciplines. IJCERT encourages conceptual, state-of-the-art, research, standard, implementation, experimental, application, and industrial case study discussions in various areas, including: computer architecture, computer networks, software engineering, information security, artificial intelligence, machine learning, data science, robotics, cyber-physical systems, the internet of things, and other areas of computer science engineering and Its Applications.
The Internet has revolutionized education and learning, presenting both opportunities and challenges with the continuous evolution of web-based technologies. The earlier version of the web, known as Web 1.0, was primarily a readonly medium, while Web 2.0 allowed for greater interactivity with read/write capabilities. Now, the emerging version of the web, Web 3.0, is considered to be a technologically advanced medium that not only facilitates read/write capabilities but also enables a machines to carry out some of the thinking that was previously expected only of humans. In a relatively short period of time, Web 2.0 and Web 3.0 have introduced new tools and technologies that have greatly facilitated web-based education and learning. This paper will explore the definition, evolution, and characteristics of Web 3.0, as well as discuss potential future technologies, trends, tools, and services that can support online learning, personalization, and knowledge construction powered by the Semantic Web.
Cryptocurrency is in great demand as an investment medium to gain financial benefits. A common problem that is often faced is how to predict the movement of the value of electronic money in the future. Investors/traders usually only see price movements and buy/sell Cryptocurrency assets intuitively, so mistakes often occur in making transactions. To anticipate and minimize this, you can use an algorithm that can help predict Cryptocurrency price movements. Extreme Learning Machine (ELM) is a development method of a simple feedforward neural network using one hidden layer or commonly known as Single Hidden Layer Feedforward Neural NetworksTesting is done by doing several trials for each percentage value, namely 60%, 65%, 70%, 75%, 80%. Tests were carried out using the binary sigmoid activation function, the number of hidden neurons was 20 and the weight range was [-1,1]. The best prediction results using MAPE are generated on Bitcoin data with the smallest error value of 2.8590% Keywords: Cryptocurrency; Investation; Extreme Learning Machine ; Prediction  Abstrak Cryptocurrency banyak diminati untuk menjadi media investasi dalam meraih keuntungan finansial. Masalah umum yang sering dihadapi adalah bagaimana meramalkan pergerakan nilai dari uang elektronik pada masa mendatan. Investor/ trader biasanya hanya melihat pergerakan harga dan melakukan jual/beli aset Cryptocurrency secara intuitif, sehingga sering terjadi salah dalam melakukan transaksi. Untuk mengantisipasi dan meminimalisir hal tersebut maka dapat menggunakan sebuah algoritme yang dapat membantu dalam meramalkan pergerakan harga Cryptocurrency . Extreme Learning Machine (ELM) merupakan metode pengembangan dari jaringan syaraf tiruan feedforward sederhana dengan menggunakan satu hidden layer atau biasa dikenal dengan Single Hidden Layer Feedforward Neural Networks . Pengujian dilakukan dengan melakukan beberapa kali percobaan untuk setiap nilai persentase yaitu 60%, 65%, 70%, 75%, 80%. Pengujian dilakukan menggunakan fungsi aktivasi sigmoid biner, jumlah hidden neuron 20 serta rentang bobot [-1,1]. Hasil prediksi terbaik menggunakan MAPE dihasilkan pada data Bitcoin dengan nilai kesalahan terkecil yaitu 2.8590% Kata Kunci: Cryptocurrency; Investasi; Extreme Learning Machine ; Prediksi
Perubahan teknologi semakin lama semakin pesat diberbagai bidang, termasuk teknologi digital yang merupakan revolusi dari teknologi analog dan elektronik. Dekade ini semua serba bermetamorfosis menjadi digital, termasuk akhirnya muncul uang digital, yaitu cryptocurrency. Cryptocurrency sebagai bentuk digital cash beroperasi dengan bantuan teknik yang disebut kriptografi. Kriptografi sendiri adalah proses yang menerjemahkan semua informasi yang dapat dibaca menjasi kode yang tidak dapat dipecah sama sekali. Cryptocurrency menggunakan blockchain sebagai buku utama, yang semua sistemnya dikelola oleh yang disebut penambang. Mata uang crypto memiliki sistem yang sedikit rumit yang tidak dengan mudah dapat dipahami, jadi pengetahuan tentang cryptocurrency mau tidak mau harus dipelajari, dipahami agar dalam implementasi tidak mengalami dampak yang merugikan. Jenis jenis cryptocurrency, serta kekurangan dan kelebihannya akan dikupas sekilas dalam artikel ini.
Blockchain has shown great potential in various fields due to its technical advantages such as peer-to-peer, timestamp, consensus algorithm and encryption. In blockchain, the protection method of transaction data or copyright is crucial and cryptographic digital signature technology has been applied as one of the copyright protection methods. The multi-signature scheme provides higher security than single-signature schemes, enhances the transparency of transactions and contracts, and is widely used in distributed systems utilizing distributed ledger technology in blockchain. Multisignature requires multiple parties to cooperate in order to produce a valid signature, reducing the risk of exposing the entire system to a single point of failure when compared to single-signature schemes. This is an important role in transactions or contracts that require consensus among multiple parties, where each party can sign to implement the agreement, increasing transparency and preventing disputes. However, the cryptographic digital signature is resource consuming and inefficient because the verification of the signature consumes lots of computational resources and excessive number of communications. Therefore, we have proposed an efficient multi-signature scheme based on Schnorr for copyright protection on Ethereum.
Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.
The growing popularity of cryptocurrencies has caused the market demand for graphics cards to reach unusual heights for their efficient cryptomining capabilities. Graphics cards are not only used for crypto mining but also video editing, video streaming, and video games, this causes an unavailability of graphics card supply due to high demand, especially for cryptomining needs and leads to unusual prices increases which makes it difficult for graphics card consumers and miners to buy graphics cards at normal price. Therefore, it is necessary to predict the price of NVIDIA graphics cards based on the influence of cryptocurrency prices. The methodology used is KDD, and the algorithm used to make predictions is SVR because its ability to overcome the overfitting problem so it can produce more accurate predictions, besides that in this study the grid search algorithm is applied to determine optimal parameters. In this study, 6 graphics cards and 2 cryptocurrencies were used which produced the 6 best prediction models which were chosen based on the RMSE value. GTX 1050 has RMSE value of 0.2028, GTX 1050 Ti has RMSE value of 0.14564, GTX 1060 has an RMSE value of 0.07629, while in the RTX 30 series, RTX 3070 has an RMSE value of 0.03178, RTX 3080 has RMSE value of 0.0388, and RTX 3090 has RMSE of 0.06259. From these results, it can be stated that RTX 30 series has better accuracy than GTX 10 series in making predictions. RBF is better than linear which only excels on the GTX 1060.