PROPOSALS FOR A BROADER APPROACH OF „MISUSE OF DEVICES AND PROGRAMS” PROVISION IN COMBATING CYBER-DEPENDENT AND CYBER-ENABLED CRIMES by Maxim DOBRINOIU

“…Although the offence in art. 6 represents the „mean-crime” in relation with the further commission of the above mentioned crimes against the confidentiality, integrity and availability of computer data and systems („purpose-crimes”), the nowadays Cybercrime phenomenon shows that the misuse of the devices and computer programs actually exceeds the legal boundaries of art. 2 to 5, and (technically) impacts much of the other forms of cyber-related or cyber-enabled offences, especially in the FinTech area, electronic payment, blockchain, cryptocurrency etc. Taking into consideration the proliferation of illegal activities against personal information, confidential data or access credentials, especially the commercialization, especially in Dark Web, of codes, passwords, hacking tools, malware and other present or future cutting-edge system interference technologies, thus posing a great danger to the whole cyber-ecosystem, an improvement of art. 6, and of all the correspondent (related) articles in the special laws or the criminal codes adopted by the signatory countries, would contribute to the creation of an extensive and much comprehensive legal tool in the prevention and efficiently combating cybercrimes.…”
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An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks by Selvam Saravanan, Uma Maheswari Balasubramanian

“…Attackers use IoT devices to create botnets and launch attacks, such as DDoS, Spamming, Cryptocurrency mining, Phishing, etc. The service providers of large-scale IoT networks need to set up a data pipeline to collect the vast network traffic data from the IoT devices, store it, analyze it, and report the malicious IoT devices and types of attacks. …”
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Examining the Long-Run and Short-Run Effects of Technical and Financial Factors on Bitcoin Blockchain Network Transaction Fees by Amin Rostami, Mahdi Safaei

“…In recent years, Bitcoin has garnered increasing public and media attention, as well as investment in this field. This cryptocurrency currently has the highest market value among existing virtual currencies that can operate in a decentralized manner based on defined incentives. …”
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Doctrinal interpretations of digitalisation as a general legal phenomenon by Emin Najafli

“…It is indicated that along with traditional law which is rapidly being digitised (moving into the virtual space from paper carriers), the phenomenon of digital law is emerging which is an intersectoral formation mediating social relations which take place in the virtual space (from cryptocurrency circulation to the provision of administrative services). …”
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Detecting anomalies in graph networks on digital markets. by Agata Skorupka

“…The article utilized three publicly accessible graph-based datasets: one describing the Twitter social network (TwiBot-20) and two describing Bitcoin cryptocurrency markets (Bitcoin OTC and Bitcoin Alpha). …”
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Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models by Quang Phung Duy, Oanh Nguyen Thi, Phuong Hao Le Thi, Hai Duong Pham Hoang, Khanh Linh Luong, Kim Ngan Nguyen Thi

“…Purpose – The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. …”
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Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations by Rohan Jha, Rishabh Jha, Mazhar Islam

“…Data centers are poised for unprecedented growth due to a revolution in Artificial Intelligence (AI), rise in cryptocurrency mining, and increasing cloud demand for data storage. …”
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Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example by Hu LI, Yunfang CHEN, Wei ZHANG

“…Designed to enhance the privacy of user transactions, mixed coin technology has created disruptions to the address clustering rules typically used for cryptocurrency regulation.Consequently, hackers have begun utilizing mixed coin technology as a tool for money laundering and fund evasion, which has raised concerns among financial regulators regarding the detection of mixed coin transactions.Currently, most detection methods for mixed coin transactions rely on data analysis and statistics, lacking a comprehensive understanding of the internal workings of these transactions.This lack of knowledge may undermine the credibility and effectiveness of detection methods due to the absence of reliable verification data.CoinJoin, a decentralized mixed coin concept, represents one approach, and commercial implementations like Wasabi have gained popularity.Drawing from the characteristics of CoinJoin and its restriction on the size of anonymous transaction sets and mixed coin amounts, a general detection method for CoinJoin mixed coin transactions was devised.Such transactions typically involved multiple inputs and outputs, with more output items than UTXOs in the input, and a high occurrence of duplicate values among the output amounts.A basic detection method for Wasabi was developed by combining the generic detection method for CoinJoin with specific features of Wasabi, as identified in related studies, to complete the detection process.A trusted validation dataset was acquired from the Wasabi platform service interface, and this dataset was analyzed to achieve two objectives.First, the alignment of rule parameters in the Wasabi base detection method was accomplished.Second, a new metric was proposed, measuring the ratio of the highest frequency of duplicate values in the output amount of transactions to the number of UTXOs in the input.This metric assessed the level of user participation in mixed coin transactions, providing a measure of user freedom.Using these two approaches, significant progress is made in the detection of mixed coin transactions.The experiments show that the recall rate of Wasabi’s basic detection method is 94.2% and the accuracy rate is 67.2%.After the analytical feedback from the credible validation dataset, the recall rate of the improved detection method reaches 100% and the accuracy rate is above 99%.The total market size of the entire CoinJoin type of mixed coin transactions was evaluated and predicted based on a common test methodology.It is concluded that the number of CoinJoin mixed coin transactions in today’s mixed coin market represents 1.9 per 1 000 of all Bitcoin transactions and 3.7 per 1 000 of the transaction value at most.…”
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