Scrypt Mining with ASICs

Cryptocurrencies have garnered a lot of attention by governments and internet enthusiasts over the past three years. These currencies are celebrated for their security and speedy transactions in a modern era of digital commerce. Bitcoin was the first of these currencies to gain a large advantage over subsequent iterations. Bitcoin was first conceived by Satoshi Nakamoto who mentioned the concept of a cryptocurrency in his paper titled Bitcoin. It featured new concepts such as proof of work and transactions which utilized hash based encryption. One particular alternative cryptocurrency is known as Litecoin. Backed by a memory intensive algorithm known as Scrypt, many cryptocurrency enthusiasts have decided to celebrate this particular coin. Scrypt expands on Bitcoin's proof of work algorithm by adding the amount of work it takes to commit a transaction within the Litecoin network. Scrypt forces more work on the device that is being used to perform the algorithm by making frequent memory requests. This makes it difficult to create specialized hardware to create new coins and to commit transactions due to the nature of memory intensive applications.Comment: Published in 201

The Future of Cybercrime: AI and Emerging Technologies Are Creating a Cybercrime Tsunami

This paper reviews the impact of AI and emerging technologies on the future of cybercrime and the necessary strategies to combat it effectively. Society faces a pressing challenge as cybercrime proliferates through AI and emerging technologies. At the same time, law enforcement and regulators struggle to keep it up. Our primary challenge is raising awareness as cybercrime operates within a distinct criminal ecosystem. We explore the hijacking of emerging technologies by criminals (CrimeTech) and their use in illicit activities, along with the tools and processes (InfoSec) to protect against future cybercrime. We also explore the role of AI and emerging technologies (DeepTech) in supporting law enforcement, regulation, and legal services (LawTech)

Blockchain Technology and Circular Economy in the environment of Total Productive Maintenance: A Natural Resource-Based View Perspective

Total Productive Maintenance (TPM) could act as a practical approach to offer sustainability deliverables in manufacturing firms aligning with the natural resource-based view (NRBV) theory’s strategic capabilities: pollution prevention, product stewardship, and sustainable development. Also, the emergence of Blockchain Technology (BCT) and Circular Economy (CE) are proven to deliver sustainable outcomes in the past literature. Therefore, the present research examines the relationship between BCT and CE and TPM’s direct and mediation effect through the lens of NRBV theory. The current study proposes a conceptual framework to examine the relationship between BCT, CE, and TPM and validates the framework through the Partial Least Squares Structural Equation Modeling. Responses from 316 Indian manufacturing firms were collected to conduct the analysis. The investigation outcomes indicate that BCT positively influences CE and TPM and that TPM has a significant positive impact on CE under the premises of NRBV theory. The results also suggest that TPM partially mediates the relationship between BCT and CE. This research fills a gap in the literature by investigating the effect of BCT and TPM on CE within the framework of the NRBV theory. It explores the link between BCT, TPM, and CE under the NRBV theory's strategic capabilities and TPM mediation. Implications - The positive influence of TPM and BCT on CE could initiate the amalgamation of BCT-TPM, improving the longevity of production equipment and products and speeding up the implementation of CE practices

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Monero Mining: CryptoNight Analysis

Το κρυπτονόμισμα Bitcoin αποτελεί την πρώτη πετυχημένη εφαρμογή της ιδέας του ηλεκτρονικού χρήματος χωρίς την διαμεσολάβηση τρίτων. Στην πορεία, πολλά κρυπτο- νομίσματα βασίστηκαν στην συγκεκριμένη τεχνολογία, εστιάζοντας το καθένα στους δικούς του στόχους και σκοπούς. Το κρυπτονόμισμα Monero είναι ένα τέτοιο εγχείρημα, βασικός σκοπός του οποίου είναι η διασφάλιση της ιδιωτικότητας και της ανωνυμίας. Σε έναν κόσμο όπου η παρακολούθηση εντείνεται, το εγχείρημα του Monero σημαίνει τον συναγερμό για την διαρκή καταπάτηση ενός εκ των θεμελιωδών ανθρώπινων δικαιωμάτων. Επιπλέον, καθώς οι επιχειρήσεις έχουν περιορίσει δραματικά τον υγιή ανταγωνισμό σχεδόν σε όλα τα διαδεδομένα κρυπτονομίσματα, το Monero προσπαθεί να τον διατηρήσει στην κοινότητά του. Ένα από τα δομικά στοιχεία του Monero είναι η διατήρηση της ισότητας μεταξύ των "ανθρακωρύχων" (miners), η οποία επιτυγχάνεται μέσω της ισονομίας (egalitarianism). Η ισονομία είναι συνέπεια μιας ιδιότητας της κρυπτογραφικής συνάρτησης που χρησιμοποιείται για την "εξόρυξη" νομισμάτων. Η συνάρτηση που χρησιμοποιείται στο Monero για αυτόν τον σκοπό λέγεται CryptoNight και είναι μέρος του CryptoNote πρωτοκόλλου. Το στοιχείο της συνάρτησης που επιτυγχάνει την ισονομία είναι μια κρυπτογραφική ιδιότητα, η οποία ονομάζεται memory-hardness. Η CryptoNight συνάρ- τηση θεωρείται ότι διαθέτει αυτήν την ιδιότητα. Όμως, μέχρι σήμερα αυτό παραμένει ισχυρισμός. Απ' όσο γνωρίζουμε, δεν υπάρχει μαθηματική απόδειξη για αυτόν τον ισχυρισμό αλλά ούτε και κάποια επίθεση που να τον διαψεύδει. Θέλοντας να ελέγξουμε την ορθότητα αυτού του ισχυρισμού, προσπαθήσαμε να κατασκευάσουμε μια μαθηματική απόδειξη. Αναφέρουμε τους λόγους για τους οποίους αποτυγχάνουμε να διατυπώσουμε μία τέτοια απόδειξη και προσπαθούμε να τους χρησι- μοποιήσουμε για να καταρρίψουμε αυτόν τον ισχυρισμό. Απ' όσο γνωρίζουμε, η παρού- σα εργασία είναι η πρώτη που μελετά αυτήν την ιδιότητα για την συνάρτηση CryptoNight και παρουσιάζεται για πρώτη φορά γραφικά η εσωτερική δομή της. Τέλος, παρουσιάζουμε την γνώση που αποκτήσαμε και ελπίζουμε αυτή η εργασία να φανεί χρήσιμη μελλοντικά σε συναδέλφους που θέλουν να συμβάλλουν στην έρευνα στο ευρύτερο πεδίο. Στόχος αυτής της έρευνας είναι να συνεισφέρει στην προσπάθεια του εγχειρήματος Monero για την διασφάλιση της ιδιωτικότητας, της ανωνυμίας και της ισότητας.Bitcoin has been a successful implementation of the concept of peer-to-peer electronic cash. Based on this technology several cryptocurrency projects have arisen, each one focusing on its purposes and goals. Monero is a decentralized cryptocurrency focusing on privacy and anonymity. In a world of surveillance, Monero raises the alarm about one of the fundamental human rights, which is continuously violated: Privacy. In addition, Monero is built to achieve equality between miners. Corporations are taking over almost every successful cryptocurrency, by making mining participation harder and harder for the hobbyists and supporters. Monero tries to keep its community clean of unhealthy competition. This is achieved through egalitarianism, which is based οn a cryptographic mining function. This function is called CryptoNight and is part of the CryptoNote protocol, the heart of Monero's structure. The feature of this function that makes it egalitarian is a cryptographic property, named memory-hardness. CryptoNight is alleged to be memory-hard. But, still today, this is just a claim. We put to the test this claim, trying to construct a formal mathematical proof, but we fail to do so. We discuss the reasons for our failure and try to use them to construct an attack on this feature. To our knowledge, we are the first to study this CryptoNight's property and the first to present graphically all the stages of CryptoNight's functionality. Finally, we present the knowledge gained and wish for this document to be useful in the future to colleagues that want to contribute in this field. The aim of this work is to contribute to Monero's fight for privacy, anonymity and equality

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029