In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Cloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field.

Blockchain‐cloud integration: Comprehensive survey and open research issues

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

Neuromorphic one-shot learning utilizing a phase-transition material.

Stochastic magnetic tunnel junctions (SMTJs) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, SMTJs exhibiting the ideal characteristics necessary for probabilistic bits ($p$-bits) are still lacking. Ideally, the SMTJs should have (a) voltage bias independence, preventing read disturbance; (b) uniform randomness in the magnetization angle between the two magnetic layers; and (c) fast fluctuations without requiring external magnetic fields, while being robust to magnetic field perturbations. Here, we propose a design that satisfies all of these requirements, using double-free-layer SMTJs with synthetic antiferromagnets (SAFs). We evaluate the proposed SMTJ design with experimentally benchmarked spin-circuit models, accounting for transport physics, coupled with the stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find that the use of low-barrier SAF layers reduces dipolar coupling, achieving uncorrelated fluctuations at zero-magnetic field, surviving up to diameters exceeding $D\ensuremath{\approx}100\phantom{\rule{0.2em}{0ex}}\mathrm{nm}$ if the nanomagnets can be made thin enough ($\ensuremath{\approx}1$--$2\phantom{\rule{0.2em}{0ex}}\mathrm{nm}$). The double-free-layer structure retains bias independence and the circular nature of the nanomagnets provides near-uniform randomness with fast fluctuations. Combining our full SMTJ model with advanced transistor models, we estimate the energy to generate a random bit to be about $3.6\phantom{\rule{0.2em}{0ex}}\mathrm{fJ}$, with fluctuation rates of about $3.3\phantom{\rule{0.2em}{0ex}}\mathrm{GHz}$ per $p$-bit. Our results will guide the experimental development of superior stochastic magnetic tunnel junctions for large-scale and energy-efficient probabilistic computation for problems relevant to machine learning and artificial intelligence. 

Double-free-layer stochastic magnetic tunnel junctions with synthetic antiferromagnets

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing.

The stochastic nature of electronic phase transitions can serve as a random number generating source. This work reports probabilistic bits ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -bits) based on hydrogen-doped vanadium oxide (H-VO2) devices with tunable insulator-metal transition (IMT) characteristics. Hydrogen donor doping reduces ground state resistance and enables control over threshold switching voltage in the post-fabricated devices. Through a paired-pulse scheme, the H-VO2 devices could generate stochastic bit sequences with adjustable switching probabilities. The sequence with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -bit value of 0.488 could pass 14 out of 15 National Institute of Standards and Technology (NIST) random and pseudorandom number generator tests. 

Hydrogenated VO2 Bits for Probabilistic Computing

Blockchain is one of the emerging technologies with the potential to disrupt many application domains. Cloud is an on-demand service paradigm facilitating the availability of shared resources for data storage and computation. In recent years, the integration of blockchain and cloud has received significant attention for ensuring efficiency, transparency, security and even for offering better cloud services in the form of novel service models. In order to exploit the full potential of blockchain-cloud integration, it is essential to have a clear understanding on the existing works within this domain. To facilitate this, there have been several survey papers, however, none of them covers the aspect of blockchain-cloud integration from a service-oriented perspective. This paper aims to fulfil this gap by providing a service oriented review of blockchain-cloud integration. Indeed, in this survey, we explore different service models into which blockchain has been integrated. For each service model, we review the existing works and present a comparative analysis so as to offer a clear and concise view in each category.

A Survey on Blockchain & Cloud Integration

Toward Variation-Tolerant Ferroelectric Neural Computing: Special Session Paper

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work, we leverage the stochastic switching of a three-state ferroelectric field-effect transistor to implement a long-term and short-term two-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28 nm high- k metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware–algorithm co-design analysis reveals the efficacy of the two-tier memory in comparison to binary stochastic synapses in on-chip learning tasks—paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones.

Hybrid Stochastic Synapses Enabled by Scaled Ferroelectric Field-effect Transistors

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Arnob Kumar Saha

Author Tools

Chat about Author

Papers

Hydrogenated VO2 Bits for Probabilistic Computing

A Survey on Blockchain & Cloud Integration

A Survey on Blockchain & Cloud Integration

Toward Variation-Tolerant Ferroelectric Neural Computing: Special Session Paper

Hybrid Stochastic Synapses Enabled by Scaled Ferroelectric Field-effect Transistors