Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such ...as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.
The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd ...sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.
Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Available observations ...generated by automated weather stations or meteorological observatories are often limited in spatial resolution resulting in misrepresentation or absence of rainfall information at these levels. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex spatio-temporal process leading to non-linear or chaotic spatio-temporal variations, no single downscaling method can be considered efficient enough. In the domains dominated by complex topographies, quasi-periodicities, and non-linearities, deep learning (DL)–based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. We employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods in this work. Summer monsoon season data from India Meteorological Department (IMD) and the tropical rainfall measuring mission (TRMM) data set were downscaled up to 4 times higher resolution using these methods. High-resolution data derived from deep learning-based models provide better results than linear interpolation for up to 4 times higher resolution. Among the three algorithms, namely, SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD-based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data post-processing, in particular, ERA5 reanalysis data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation. This study is the first step towards developing deep learning-based weather data downscaling model for Indian summer monsoon rainfall data.
The modern world’s increasing reliance on automated systems for everyday tasks has resulted in a corresponding rise in power consumption. The demand is further augmented by increased sales of ...electric vehicles, smart cities, smart transportation, etc. This growing dependence underscores the critical necessity for a robust smart energy measurement and management system to ensure a continuous and efficient power supply. However, implementing such a system presents a set of challenges, particularly concerning the transparency, security, and trustworthiness of data storage and retrieval. Blockchain technology offers an innovative solution in the form of a distributed ledger, which guarantees secure and transparent transaction storage and retrieval. This research introduces a blockchain-based system, utilising Hyperledger Fabric and smart contracts, designed for the secure storage and retrieval of consumers’ energy consumption data. Finally, a user-friendly web portal was designed and developed using the node.js framework, offering an accessible and intuitive interface to monitor and manage energy consumption effectively.
The spectrum of Internet of Things (IoT) applications is vast. It serves in various domains such as smart homes, intelligent buildings, health care, emergency response, and many more, reflecting the ...exponential market penetration of the IoT. Various security threats have been made to modern-day systems. Cyberattacks have seen a marked surge in frequency, particularly in recent times. The growing concern centers around the notable rise in cloning attacks, persisting as a significant and looming threat. In our work, an in-depth survey on the IoT that employs physically unclonable functions (PUFs) was conducted. The first contribution analyzes PUF-based authentication, communication protocols, and applications. It also tackles the eleven challenges faced by the research community, proposes solutions to these challenges, and highlights cloning attacks. The second contribution suggests the implementation of a framework model known as PUF3S-ML, specifically crafted for PUF authentication in the Internet of Things (IoT), incorporating innovative lightweight encryption techniques. It focuses on safeguarding smart IoT networks from cloning attacks. The key innovation framework comprises three stages of PUF authentication with IoT devices and an intelligent cybersecurity monitoring unit for IoT networks. In the methodology of this study, a survey relevant to the concerns was conducted. More data were provided previously regarding architecture, enabling technologies, and IoT challenges. After conducting an extensive survey of 125 papers, our analysis revealed 23 papers directly relevant to our domain. Furthermore, within this subset, we identified 11 studies specifically addressing the intersection of communication protocols with PUFs. These findings highlight the targeted relevance and potential contributions of the existing literature to our research focus.
Data security and privacy concerns on the Internet are continuously rising considering security breaches. These security breaches are often due to the presence of host(s) infected with malware called ...bots. As a result, bot-infection detection in enterprise networks is getting a greater research focus. From the perspective of law enforcement, the focus is to detect and destroy the botnet infrastructure which comprises mostly of Command and Control server along with the technique used for communication. From an enterprise perspective, it is important to detect and quarantine bot-infected machines thereby preventing the chance of any security breach. While many efforts have been made to detect malicious domains, limited research is done to detect infected machines in an enterprise network. This paper presents a deep learning-based technique for detecting bot-infected machines in a network applied to the hourly hosts' Domain Name System (DNS) fingerprint. Multi feature anomaly detection technique was implemented to detect bots in the campus network thereby minimizing the number of false positives. The results indicate a significant improvement over previous work. Finally, a GUI tool named DeepDAD is presented to facilitate investigating and detecting bots in a network traffic using DNS traffic analysis.
Abstract
The Madden Julian Oscillation (MJO), the dominant subseasonal variability in the tropics, is widely represented using the Real-time Multivariate MJO (RMM) index. The index is limited to the ...satellite era (post-1974) as its calculation relies on satellite-based observations. Oliver and Thompson (J Clim 25:1996–2019, 2012) extended the RMM index for the twentieth century, employing a multilinear regression on the sea level pressure (SLP) from the NOAA twentieth century reanalysis. They obtained an 82.5% correspondence with the index in the satellite era. In this study, we show that the historical MJO index can be successfully reconstructed using machine learning techniques and improved upon. We obtain a significant improvement of up to 4%, using the support vector regressor (SVR) and convolutional neural network (CNN) methods on the same set of predictors used by Oliver and Thompson. Based on the improved RMM indices, we explore the long-term changes in the intensity, phase occurrences, and frequency of the winter MJO events during 1905–2015. We show an increasing trend in MJO intensity (22–27%) during this period. We also find a multidecadal change in MJO phase occurrence and periodicity corresponding to the Pacific Decadal Oscillation (PDO), while the role of anthropogenic warming cannot be ignored.
► Comparison of bacterial cellulose production in three different complex media. ► Effect of media composition on structural and spatial arrangement of cellulose. ► FTIR analysis for determination of ...crystallinity of cellulose. ► Fractional factorial design for bacterial cellulose production from A. aceti MTCC 2623.
Acetobacter aceti MTCC 2623 was studied as an alternative microbial source for bacterial cellulose (BC) production. Effect of media components on cell growth rate, BC production and cellulose characteristics were studied. FTIR results showed significant variations in cellulose characteristics produced by A. aceti in different media. Results have shown the role of fermentation time on crystallinity ratio of BC in different media. Further, effect of six different media components on cell growth and BC production was studied using fractional factorial design. Citric acid was found to be the most significant media component for cell growth rate (95% confidence level, R2=0.95). However, direct role of these parameters on cellulose production was not established (p-value>0.05).
Mobile spam messages have become one of the main concerns in the field of short messaging service (SMS) due to its negative impact on mobile users and networks. The current literature lacks effective ...solutions for this issue. In this study, the negative impacts of SMS spam were thoroughly analysed, and the existing techniques for SMS spam detection were investigated through two experiments. The first experiment was performed to test and compare the current data mining and cost-sensitive techniques, whereas the second experiment was conducted to test the performance of the proposed technique. Based on the experimental results of the first phase, the most optimal non-cost classifier is a Bayesian network classifier, which is well behaved under the cost-sensitive classifier and obtained the lowest rate of false negative and an acceptable false positive rate. The proposed strategy achieves the best performance in terms of false negative SMS spam classification, obtaining the smallest total expenses and highest precision amongst the compared strategies.
This research sheds light on the nature of an advanced persistent threat (APT) on smartphones by analysing real APT attack cases targeting smartphone users. Based on the research, context-aware ...access control is the best technique to minimise APT instigated by social engineering attacks in smartphones. Therefore, this research proposes an access control model known as sentient-based access control model (SENSATE), which combines role- and attribute-based and multi-level security to maintain information integrity and confidentiality that can be infringed through social engineering attacks. The implementation of existing smartphone sensors in the design of SENSATE is a novel approach in the fight against smartphone cybercrimes, such as APT.
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