Relevant bibliographies by topics / Cryptomining

Academic literature on the topic 'Cryptomining'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Cryptomining"

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Maulana, Mochammad Ichsan, Asep Dede Kurnia, and Ayi Nurbaeti. "Studi Kajian Bisnis Tambang Uang Digital (Cryptomining) Dalam Konteks Ijarah." EKSISBANK: Ekonomi Syariah dan Bisnis Perbankan 5, no. 1 (June 27, 2021): 35–56. http://dx.doi.org/10.37726/ee.v5i1.158.

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Revolusi industri 4.0 memunculkan mata uang digital cryptocurrency, serta penambang (miner) yang berfungsi sebagai pihak penengah untuk memvalidasi tiap transaksi. Penelitian ini bertujuan untuk mengetahui mekanisme Cryptomining dan mengetahui kegiatan Cryptomining dalam tinjauan akad ijarah. Ada pun tahapan-tahapannya adalah; merakit mining rig, screening koin, pengaturan aplikasi, memilih pool, lalu melakukan proses menambang disertai maintenance, hingga akhirnya menerima imbalan/reward. Penelitian dilakukan dengan metode kualitatif. Penulis mengumpulkan data dengan cara terjun langsung sebagai pelaku Cryptomining. Peneliti mengambil referensi dan poin-poin yang terdapat dalam fatwa-fatwa MUI untuk melakukan analisa dan pengembangan hipotesis dalam penyetaraan rukun-rukun akad Ijarah terhadap proses dalam kegiatan Cryptomining. Hasil penelitian ini menunjukkan bahwa unsur-unsur kesetaraan kegiatan tambang uang digital (Cryptomining) sebagian besar sesuai dengan rukun-rukun akad Ijarah. Para Miner disetarakan dengan Mu’jir, pengguna cryptocurrency sebagai musta’jir, transaksi yang terproses sebagai manfa’ah, dan reward sebagai ujrah. Kegiatan mining itu sendiri merupakan kegiatan bermuamalah yang sebagian besar dilakukan secara digital lewat media internet. Pun begitu, masih terdapat kerancuan pada bagian ketentuan pembayaran ujrah. Antara lain ketentuan ‘harus bersifat tunai’ yang terdapat pada DSN MUI NO:28/DSN-MUI/III/2002 tentang jual beli mata uang, (yang dalam hal ini berkonteks kepada medium pembayaran ujrah). Lebih lanjut tentang hal ini membutuhkan ijtihad dari ulama, khususnya DSN MUI.
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Gangwal, Ankit, and Mauro Conti. "Cryptomining Cannot Change Its Spots: Detecting Covert Cryptomining Using Magnetic Side-Channel." IEEE Transactions on Information Forensics and Security 15 (2020): 1630–39. http://dx.doi.org/10.1109/tifs.2019.2945171.

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Caprolu, Maurantonio, Simone Raponi, Gabriele Oligeri, and Roberto Di Pietro. "Cryptomining makes noise: Detecting cryptojacking via Machine Learning." Computer Communications 171 (April 2021): 126–39. http://dx.doi.org/10.1016/j.comcom.2021.02.016.

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Pastor, Antonio, Alberto Mozo, Stanislav Vakaruk, Daniele Canavese, Diego R. Lopez, Leonardo Regano, Sandra Gomez-Canaval, and Antonio Lioy. "Detection of Encrypted Cryptomining Malware Connections With Machine and Deep Learning." IEEE Access 8 (2020): 158036–55. http://dx.doi.org/10.1109/access.2020.3019658.

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Malik, Asad Waqar, and Zahid Anwar. "Do Charging Stations Benefit from Cryptojacking? A Novel Framework for Its Financial Impact Analysis on Electric Vehicles." Energies 15, no. 16 (August 9, 2022): 5773. http://dx.doi.org/10.3390/en15165773.

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Electric vehicles (EVs) are becoming popular due to their efficiency, eco-friendliness, and the increasing cost of fossil fuel. EVs support a variety of apps because they house powerful processors and allow for increased connectivity. This makes them an attractive target of stealthy cryptomining malware. Recent incidents demonstrate that both the EV and its communication model are vulnerable to cryptojacking attacks. The goal of this research is to explore the extent to which cryptojacking impacts EVs in terms of recharging and cost. We assert that while cryptojacking provides a financial advantage to attackers, it can severely degrade efficiency and cause battery loss. In this paper we present a simulation model for connected EVs, the cryptomining software, and the road infrastructure. A novel framework is proposed that incorporates these models and allows an objective quantification of the extent of this economic damage and the advantage to the attacker. Our results indicate that batteries of infected cars drain more quickly than those of normal cars, forcing them to return more frequently to the charging station for a recharge. When just 10% of EVs are infected we observed 70.6% more refueling requests. Moreover, if the hacker infects a charging station then he can make a USD 436.4 profit per day from just 32 infected EVs. Overall, our results demonstrate that cryptojackers injected into EVs indirectly provide a financial advantage to the charging stations at the cost of an increased energy strain on society.
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Karn, Rupesh Raj, Prabhakar Kudva, Hai Huang, Sahil Suneja, and Ibrahim M. Elfadel. "Cryptomining Detection in Container Clouds Using System Calls and Explainable Machine Learning." IEEE Transactions on Parallel and Distributed Systems 32, no. 3 (March 1, 2021): 674–91. http://dx.doi.org/10.1109/tpds.2020.3029088.

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Darabian, Hamid, Sajad Homayounoot, Ali Dehghantanha, Sattar Hashemi, Hadis Karimipour, Reza M. Parizi, and Kim-Kwang Raymond Choo. "Detecting Cryptomining Malware: a Deep Learning Approach for Static and Dynamic Analysis." Journal of Grid Computing 18, no. 2 (January 21, 2020): 293–303. http://dx.doi.org/10.1007/s10723-020-09510-6.

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Lian, Wenjuan, Guoqing Nie, Yanyan Kang, Bin Jia, and Yang Zhang. "Cryptomining malware detection based on edge computing-oriented multi-modal features deep learning." China Communications 19, no. 2 (February 2022): 174–85. http://dx.doi.org/10.23919/jcc.2022.02.014.

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Hernandez-Suarez, Aldo, Gabriel Sanchez-Perez, Linda K. Toscano-Medina, Jesus Olivares-Mercado, Jose Portillo-Portilo, Juan-Gerardo Avalos, and Luis Javier García Villalba. "Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks." Applied Sciences 12, no. 7 (March 22, 2022): 3234. http://dx.doi.org/10.3390/app12073234.

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With the growing popularity of cryptocurrencies, which are an important part of day-to-day transactions over the Internet, the interest in being part of the so-called cryptomining service has attracted the attention of investors who wish to quickly earn profits by computing powerful transactional records towards the blockchain network. Since most users cannot afford the cost of specialized or standardized hardware for mining purposes, new techniques have been developed to make the latter easier, minimizing the computational cost required. Developers of large cryptocurrency houses have made available executable binaries and mainly browser-side scripts in order to authoritatively tap into users’ collective resources and effectively complete the calculation of puzzles to complete a proof of work. However, malicious actors have taken advantage of this capability to insert malicious scripts and illegally mine data without the user’s knowledge. This cyber-attack, also known as cryptojacking, is stealthy and difficult to analyze, whereby, solutions based on anti-malware extensions, blocklists, JavaScript disabling, among others, are not sufficient for accurate detection, creating a gap in multi-layer security mechanisms. Although in the state-of-the-art there are alternative solutions, mainly using machine learning techniques, one of the important issues to be solved is still the correct characterization of network and host samples, in the face of the increasing escalation of new tampering or obfuscation techniques. This paper develops a method that performs a fingerprinting technique to detect possible malicious sites, which are then characterized by an autoencoding algorithm that preserves the best information of the infection traces, thus, maximizing the classification power by means of a deep dense neural network.
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Mozo, Alberto, Antonio Pastor, Amit Karamchandani, Luis de la Cal, Diego Rivera, and Jose Ignacio Moreno. "Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range." Applied Sciences 12, no. 20 (October 14, 2022): 10349. http://dx.doi.org/10.3390/app122010349.

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Cybercrime has become more pervasive and sophisticated over the years. Cyber ranges have emerged as a solution to keep pace with the rapid evolution of cybersecurity threats and attacks. Cyber ranges have evolved to virtual environments that allow various IT and network infrastructures to be simulated to conduct cybersecurity exercises in a secure, flexible, and scalable manner. With these training environments, organizations or individuals can increase their preparedness and proficiency in cybersecurity-related tasks while helping to maintain a high level of situational awareness. SPIDER is an innovative cyber range as a Service (CRaaS) platform for 5G networks that offer infrastructure emulation, training, and decision support for cybersecurity-related tasks. In this paper, we present the integration in SPIDER of defensive exercises based on the utilization of machine learning models as key components of attack detectors. Two recently appeared network attacks, cryptomining using botnets of compromised devices and vulnerability exploit of the DoH protocol (DNS over HTTP), are used as the support use cases for the proposed exercises in order to exemplify the way in which other attacks and the corresponding ML-based detectors can be integrated into SPIDER defensive exercises. The two attacks were emulated, respectively, to appear in the control and data planes of a 5G network. The exercises use realistic 5G network traffic generated in a new environment based on a fully virtualized 5G network. We provide an in-depth explanation of the integration and deployment of these exercises and a complete walkthrough of them and their results. The machine learning models that act as attack detectors are deployed using container technology and standard interfaces in a new component called Smart Traffic Analyzer (STA). We propose a solution to integrate STAs in a standardized way in SPIDER for the use of trainees in exercises. Finally, this work proposes the application of Generative Adversarial Networks (GANs) to obtain on-demand synthetic flow-based network traffic that can be seamlessly integrated into SPIDER exercises to be used instead of real traffic and attacks.
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Books on the topic "Cryptomining"

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Eliet, jean, and gaudin alexandre. Cryptominig: Comment Se Créer des Sources de Revenues Passifs en Minant de la Crypto-Monnaie. Independently Published, 2022.

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Book chapters on the topic "Cryptomining"

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Gangwal, Ankit, Samuele Giuliano Piazzetta, Gianluca Lain, and Mauro Conti. "Detecting Covert Cryptomining Using HPC." In Cryptology and Network Security, 344–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65411-5_17.

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Jougleux, Philippe. "Legal Aspects of Malicious Cryptomining in the EU." In EU Internet Law in the Digital Single Market, 339–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69583-5_14.

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Conference papers on the topic "Cryptomining"

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Carlin, Domhnall, Philip OrKane, Sakir Sezer, and Jonah Burgess. "Detecting Cryptomining Using Dynamic Analysis." In 2018 16th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2018. http://dx.doi.org/10.1109/pst.2018.8514167.

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Tekiner, Ege, Abbas Acar, A. Selcuk Uluagac, Engin Kirda, and Ali Aydin Selcuk. "In-Browser Cryptomining for Good: An Untold Story." In 2021 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS). IEEE, 2021. http://dx.doi.org/10.1109/dapps52256.2021.00008.

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Wang, Peiran, Yuqiang Sun, Cheng Huang, Yutong Du, Genpei Liang, and Gang Long. "MineDetector: JavaScript Browser-side Cryptomining Detection using Static Methods." In 2021 IEEE 24th International Conference on Computational Science and Engineering (CSE). IEEE, 2021. http://dx.doi.org/10.1109/cse53436.2021.00022.

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Kelton, Conor, Aruna Balasubramanian, Ramya Raghavendra, and Mudhakar Srivatsa. "Browser-Based Deep Behavioral Detection of Web Cryptomining with CoinSpy." In Workshop on Measurements, Attacks, and Defenses for the Web. Reston, VA: Internet Society, 2020. http://dx.doi.org/10.14722/madweb.2020.23002.

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Mani, Ganapathy, Vikram Pasumarti, Bharat Bhargava, Faisal Tariq Vora, James MacDonald, Justin King, and Jason Kobes. "DeCrypto Pro: Deep Learning Based Cryptomining Malware Detection Using Performance Counters." In 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE, 2020. http://dx.doi.org/10.1109/acsos49614.2020.00032.

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Zhang, Shize, Zhiliang Wang, Jiahai Yang, Xin Cheng, XiaoQian Ma, Hui Zhang, Bo Wang, Zimu Li, and Jianping Wu. "MineHunter: A Practical Cryptomining Traffic Detection Algorithm Based on Time Series Tracking." In ACSAC '21: Annual Computer Security Applications Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3485832.3485835.

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Li, Zhi, Weijie Liu, Hongbo Chen, XiaoFeng Wang, Xiaojing Liao, Luyi Xing, Mingming Zha, Hai Jin, and Deqing Zou. "Robbery on DevOps: Understanding and Mitigating Illicit Cryptomining on Continuous Integration Service Platforms." In 2022 IEEE Symposium on Security and Privacy (SP). IEEE, 2022. http://dx.doi.org/10.1109/sp46214.2022.9833803.

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Lew, Roger, Ronald Boring, and Thomas Ulrich. "Envisioning 21st Century Mixed-Initiative Operations for Energy Systems." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002137.

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Despite a slow pace, Nuclear Power is undergoing a global renaissance. Small modular reactors (SMR) and microreactors are in various design and commissioning phases. These are designed to be built in factories and installed onsite, providing a means to rapidly deploy nuclear power while controlling for uncertain capital expenditures and cost overruns. The OECD (2016) is projecting that by 2035 we could have 21 GWe of new nuclear electricity capacity installed globally with 3.5 GWe in the United States.Simultaneously, renewables such as wind and solar are growing exponentially and battery electric vehicles are gaining traction in the energy sector. If vehicles transition to battery electric vehicles (BEV) our electricity consumption would roughly double. The energy grid as a whole is evolving as numerous point source generators come online and smarter grids enable better resource management and dynamic pricing. The result will be a distributed energy market where individuals and utilities both buy and sell resources in a fast-paced, brokered market. Or perhaps more accurately, autonomous agents will buy and sell resources on behalf of utilities, individuals, and intermediaries.The pertinent question then becomes how do we have human oversight of resources to maintain safe, secure, and reliable operation?A reasonable approach is to examine assets as three general classes. The first class comprises commodity consumer-oriented devices such as home solar, battery storage, and BEVs represented distributed nano-scale devices. The capital expenditures of any single device or installation are relatively small, and the potential consequences of a single installation failing are relatively small. Minimal regulatory oversight is required for individual installations. The second class comprises distributed micro-scale devices like nuclear micro-reactors and small modular reactors. These will have substantial automation compared to existing Generation II reactors. They could incorporate remote operations and monitoring at the fleet scale, with the ability to shut down systems locally. Disruptions would have costly impacts to an organization or municipality.Lastly, at the other end of the spectrum are high-value assets with the potential for low-probability high consequence events. These would include gigawatt-scale nuclear/solar/hydro plants that might also have flexible operations to support onsite data centers, hydrogen production, or cryptomining. These assets would be high-value targets and distruptions would have the potential for severe economic, environmental, and functional consequences at large geographic scales. When we start thinking about human oversight, participation, and decision making, the first class is consumer-oriented. Consumers will be enabled to become prosumers (producers and consumers) sell excess or optimize energy usage and storage based on dynamic rates.The third class of high-value assets resembles how critical infrastructure is managed today. These high-value assets are conservative and slow to evolve through the adoption of automation and operational changes. They would still need to maintain high degrees of human vigilance compared to the other systems for regulatory adherence and maintaining cyber-physical security and reliability.The second class still has high regulatory requirements. However, it is a bit of a clean slate to conceptualize operations and monitoring from first principles with high levels of automation and mixed-initiative monitoring and control (AI/human teaming). In this paper we explore those possibilities.New SMR and microreactors incorporate passive safety and modern engineering modeling and analysis that wasn't available during the design and commisioning of Generation II reactors. The result is reactors that have significantly reduced risks of catastrophic melt-down events like Fukishima. This dramatically expands the possibilities for how they can be monitored and controlled. When we ponder what modern nuclear control rooms should look like we envision multiple operators monitoring dozens of screens to maintain situational awareness and readiness to respond at a moments notice. However, this is unlikely and perhaps even undersired. Once reactors, in particular microreactors, have the demonstrated capability of operating hands-free with minimal oversight it becomes misguided to install humans to maintain constant vigilance (e.g. Level 4 to 5 self-driving). The key performance indicator should be system performance not situational awareness. Having "operators" permanently installed in a control room when no action is required 99.9% of the time becomes a superficial level of vigilance. Take system administration as a corollary. System administrator's primary responsibility is to maintain the availability of infrastructure, but their primary tasking is not to sit idly by and actively monitor.
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