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This paper provides study of behaviour on a drive-by crypto-jacking, specifically Coinhive's Monero. Crypto-jacking is a form of cyber threat where a host machine's processing power hijacked thru infected website to solve cryptographic puzzles as an unwitting participant. The sample study share host machine and network behaviours when host visited websites that have been embedded with Coinhive's Monero related script. These data collection of behaviour can be utilize to identify and detect Coinhive's Monero mining activities in networks.

Blockchain technology, known for its decentralized, trustless, and immutable nature, is gaining traction across various industries. As Blockchain 3.0 is expected to see widespread adoption in the sharing economy and energy trading, its associated energy consumption could have significant environmental implications. This study introduces an environmentally extended input–output (EEIO) model to assess the environmental impacts—such as freshwater use, PM2.5 emissions, CO 2 emissions, atmospheric Hg, and solid waste generation—linked to blockchain‐driven electricity demand. By analyzing scenarios based on projected cryptocurrency hash rates and hardware efficiencies, the study evaluates how large‐scale blockchain adoption affects resource consumption and environmental emissions across sectors. The results show that Monero’s design leads to higher resource consumption and environmental impact, with freshwater use being the most affected, followed by greenhouse gas emissions and PM2.5 levels.

Due to the scarcity of spectrum and the abuse of idle spectrum, it is very important to share idle spectrum to achieve greater utilization of spectrum. Unfortunately, the existing research hardly considers the security problem and privacy protection of spectrum trading in the dynamic spectrum sharing (DSS) system. Thus, we propose a secure and conditional privacy-preserving blockchain-based spectrum trading scheme in a DSS system. We exploit blockchain and ring signature to build a spectrum sharing system to solve the spectrum access and management issues. In our scheme, primarily, Monero currency is used to trade the spectrum between users for ensuring the fairness of the transaction. Besides, the ring signature on blockchain can realize all legal users’ anonymity during the spectrum trading. Furthermore, we design an ID-based traceable ring signature to achieve conditional anonymity, which can avoid malicious users from disturbing normal transactions. The security analysis shows that our scheme can realize users’ conditional anonymity, unforgeability, and transactions’ traceability. The performance evaluation demonstrates that the computational overhead of our scheme in the signature, verification and tracing process does not exceed 15 % , 60 % and 90 % of that observed in related works, respectively.

Cryptojacking is a type of computer piracy in which a hacker uses a victim’s computer resources, without their knowledge or consent, to mine for cryptocurrency. This is made possible by new memory-based cryptomining techniques and the growth of new web technologies such as WebAssembly, allowing mining to occur within a browser. Most of the research in the field of cryptojacking has focused on detection methods rather than prevention methods. Some of the detection methods proposed in the literature include using static and dynamic features of in-browser cryptojacking malware, along with machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and others. However, these methods can be effective in detecting known cryptojacking malware, but they may not be able to detect new or unknown variants. The existing prevention methods are shown to be effective only against web-assembly (WASM)-based cryptojacking malware and cannot handle mining service-providing scripts that use non-WASM modules. This paper proposes a novel hybrid approach for detecting and preventing web-based cryptojacking. The proposed approach performs the real-time detection and prevention of in-browser cryptojacking malware, using the blacklisting technique and statistical code analysis to identify unique features of non-WASM cryptojacking malware. The experimental results show positive performances in the ease of use and efficiency, with the detection accuracy improved from 97% to 99.6%. Moreover, the time required to prevent already known malware in real time can be decreased by 99.8%.

Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. Experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard’s detection rate for mining pages is 96%, but MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot.

High Accessibility and Easy Investment makes Cryptocurrency an important income source for many people. Cryptocurrency is a kind of Digital/Virtual currency which is created using blockchain Technology and is protected by Cryptography. Cryptocurrencies enables users to Accept, Transfer and request the capital between the Users without the requirement of intermediaries such as banks. Now a day many Cryptocurrencies are available across the world such as Bitcoin, Litecoin, Monero, Dogecoin etc. This study is more determined over a very famous and demanding Cryptocurrency known as Bitcoin over the past years. Here, firstly we make an effort to predict the price of bitcoin by examining numerous numbers of parameters that affect the cost of bitcoin. Different kinds of Machine learning models will be used to estimate the price of Bitcoin. This study provides the accuracy and precision of each model that are used in this study and determine the suitable method to estimate the price more accurately.

This paper analyses three important issues regarding Blockchain systems. The first one is related to the existence, success and mitigation of silent mining activity achieved through the development of Application-specific Integrated Circuits (ASICs). The second one lies in the mathematical modelling of Blockchain systems affected by ASIC mining machines and in the mathematical modelling of Blockchain with suppressed ASICs. Finally, this paper presents the economic parameters related to the rate of Return on Investment (ROI) and the possibility of calculating them based on the obtained results. Three different Blockchain systems were analysed, two of which allow the usage of ASIC machines, while one of them by definition does not support this type of mining activity. The analysis showed that the systems which involve ASIC machines can be described by means of linear regression models, while suppressing ASICs mining would provide a different statistical model. Successful mitigation activities can provide reliable data for the calculation of the economic parameters related to the rate of Return on Investment (ROI) based on silent mining.

Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims’ computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between compromised systems and remote mining pools using the de facto standard protocol Stratum. While prior research primarily focused on endpoint-based detection of in-browser mining, in this paper, we address network-based detection of cryptomining malware in general. We propose XMR-Ray, a machine learning detector using novel features based on reconstructing the Stratum protocol from raw NetFlow records. Our detector is trained offline using only mining traffic and does not require privacy-sensitive normal network traffic, which facilitates its adoption and integration.In our experiments, XMR-Ray attained 98.94% detection rate at 0.05% false alarm rate, outperforming the closest competitor. Our evaluation furthermore demonstrates that it reliably detects previously unseen mining pools, is robust against common obfuscation techniques such as encryption and proxies, and is applicable to mining in the browser or by compiled binaries. Finally, by deploying our detector in a large university network, we show its effectiveness in protecting real-world systems.

Since the inauguration of cryptocurrencies, Bitcoin has been under pressure from competing tokens. As Bitcoin is a public open ledger blockchain coin, it has its weaknesses in privacy and anonymity. In the recent decade numerous coins have been initiated as privacy coins, which try to tackle these weaknesses. This research compares mostly mature privacy coins to Bitcoin, and comparison is made from a price perspective. It seems that Bitcoin is leading privacy coins in price terms, and correlation is typically high and positive. From the earlier crypto market peak of 2017-18, only a very small number of coins are showing positive returns in 2021. It is typical that many privacy coins have lost substantial amounts of their value (ranging 80-90%) or that they do not exist anymore at all. Only Horizen and Monero have shown long-term sustainability in their value; however, their price changes follow that of Bitcoin very closely. The role of privacy coins in the future remains as an open issue.