arXiv subjects

Thippa Reddy Gadekallu

Publications and source records attributed to Thippa Reddy Gadekallu.

At least 37 records · Page 2Linked to original sources

arXiv2022

Blockchain for the Metaverse: A Review

Since Facebook officially changed its name to Metaverse in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users' digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments towards the use of blockchain in the metaverse in the future.

cs.SI↗

arXiv2021

Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyper-parameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper-parameters and genetically modifies the parameters cluster-wise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyper-parameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data.

cs.AI↗

arXiv2021

Blockchain for Edge of Things: Applications, Opportunities, and Challenges

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, called BEoT that has been regarded as a promising enabler for future services and applications. In this paper, we present a state-of-the-art review of recent developments in BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home and smart grid. Security challenges in BEoT paradigm are also discussed and analyzed, with some key services such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

cs.CR↗

arXiv2021

Blockchain based Attack Detection on Machine Learning Algorithms for IoT based E-Health Applications

The application of machine learning (ML) algorithms are massively scaling-up due to rapid digitization and emergence of new tecnologies like Internet of Things (IoT). In today's digital era, we can find ML algorithms being applied in the areas of healthcare, IoT, engineering, finance and so on. However, all these algorithms need to be trained in order to predict/solve a particular problem. There is high possibility of tampering the training datasets and produce biased results. Hence, in this article, we have proposed blockchain based solution to secure the datasets generated from IoT devices for E-Health applications. The proposed blockchain based solution uses using private cloud to tackle the aforementioned issue. For evaluation, we have developed a system that can be used by dataset owners to secure their data.

cs.CR↗

arXiv2021

Security in Next Generation Mobile Payment Systems: A Comprehensive Survey

Cash payment is still king in several markets, accounting for more than 90\ of the payments in almost all the developing countries. The usage of mobile phones is pretty ordinary in this present era. Mobile phones have become an inseparable friend for many users, serving much more than just communication tools. Every subsequent person is heavily relying on them due to multifaceted usage and affordability. Every person wants to manage his/her daily transactions and related issues by using his/her mobile phone. With the rise and advancements of mobile-specific security, threats are evolving as well. In this paper, we provide a survey of various security models for mobile phones. We explore multiple proposed models of the mobile payment system (MPS), their technologies and comparisons, payment methods, different security mechanisms involved in MPS, and provide analysis of the encryption technologies, authentication methods, and firewall in MPS. We also present current challenges and future directions of mobile phone security.

cs.CR↗

arXiv2021

Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions

The rapid development of the Internet and smart devices trigger surge in network traffic making its infrastructure more complex and heterogeneous. The predominated usage of mobile phones, wearable devices and autonomous vehicles are examples of distributed networks which generate huge amount of data each and every day. The computational power of these devices have also seen steady progression which has created the need to transmit information, store data locally and drive network computations towards edge devices. Intrusion detection systems play a significant role in ensuring security and privacy of such devices. Machine Learning and Deep Learning with Intrusion Detection Systems have gained great momentum due to their achievement of high classification accuracy. However the privacy and security aspects potentially gets jeopardised due to the need of storing and communicating data to centralized server. On the contrary, federated learning (FL) fits in appropriately as a privacy-preserving decentralized learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system. In order to establish the need for FL, various types of IDS, relevant ML approaches and its associated issues are discussed. The paper presents detailed overview of the implementation of FL in various aspects of anomaly detection. The allied challenges of FL implementations are also identified which provides idea on the scope of future direction of research. The paper finally presents the plausible solutions associated with the identified challenges in FL based intrusion detection system implementation acting as a baseline for prospective research.

cs.CR↗

arXiv2021

Genetically Optimized Prediction of Remaining Useful Life

The application of remaining useful life (RUL) prediction has taken great importance in terms of energy optimization, cost-effectiveness, and risk mitigation. The existing RUL prediction algorithms mostly constitute deep learning frameworks. In this paper, we implement LSTM and GRU models and compare the obtained results with a proposed genetically trained neural network. The current models solely depend on Adam and SGD for optimization and learning. Although the models have worked well with these optimizers, even little uncertainties in prognostics prediction can result in huge losses. We hope to improve the consistency of the predictions by adding another layer of optimization using Genetic Algorithms. The hyper-parameters - learning rate and batch size are optimized beyond manual capacity. These models and the proposed architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized architecture can predict the given hyper-parameters autonomously and provide superior results.

cs.AI↗

arXiv2021

Roughsets-based Approach for Predicting Battery Life in IoT

Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water. Firstly, the missing values in the data are replaced with the attribute mean. Later, one-hot encoding technique is applied for achieving data homogeneity followed by the standard scalar technique to normalize the data. Then, rough set theory is used for feature extraction, and the resultant data is fed into a Deep Neural Network (DNN) model for the optimized prediction results. The proposed model is then compared with the state of the art machine learning models and the results justify its superiority on the basis of performance metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Test Variance Score.

cs.LG↗

arXiv2021

A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions

Big data has generated strong interest in various scientific and engineering domains over the last few years. Despite many advantages and applications, there are many challenges in big data to be tackled for better quality of service, e.g., big data analytics, big data management, and big data privacy and security. Blockchain with its decentralization and security nature has the great potential to improve big data services and applications. In this article, we provide a comprehensive survey on blockchain for big data, focusing on up-to-date approaches, opportunities, and future directions. First, we present a brief overview of blockchain and big data as well as the motivation behind their integration. Next, we survey various blockchain services for big data, including blockchain for secure big data acquisition, data storage, data analytics, and data privacy preservation. Then, we review the state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart city, smart healthcare, smart transportation, and smart grid. For a better understanding, some representative blockchain-big data projects are also presented and analyzed. Finally, challenges and future directions are discussed to further drive research in this promising area.

cs.CR↗

arXiv2021

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Generally, the risks associated with malicious threats are increasing for the IIoT and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the IIoT network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naive Bayes are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model's classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.

cs.CR↗

arXiv2021

Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey

Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids and fault detectors), load management (smart meters and smart electric appliances). Thanks to recent advancements in big data and computing technologies, Deep Learning (DL) can be leveraged to learn the patterns from the generated data and predict the demand for electricity and peak hours. Motivated by the advantages of deep learning in smart grids, this paper sets to provide a comprehensive survey on the application of DL for intelligent smart grids and demand response. Firstly, we present the fundamental of DL, smart grids, demand response, and the motivation behind the use of DL. Secondly, we review the state-of-the-art applications of DL in smart grids and demand response, including electric load forecasting, state estimation, energy theft detection, energy sharing and trading. Furthermore, we illustrate the practicality of DL via various use cases and projects. Finally, we highlight the challenges presented in existing research works and highlight important issues and potential directions in the use of DL for smart grids and demand response.

cs.LG↗

arXiv2021

Fusion of Federated Learning and Industrial Internet of Things: A Survey

Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, federated learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributive and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning, deep learning and blockchain techniques for FL in secure IIoT. Further we analyze and summarize the ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in healthcare and automobile industry. Finally, we shed light on challenges, some possible solutions and potential directions for future research.

cs.NI↗

arXiv2020

A Framework for Prediction and Storage of Battery Life in IoT Devices using DNN and Blockchain

As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.

cs.CR↗

arXiv2020

An Incentive Based Approach for COVID-19 using Blockchain Technology

The current situation of COVID-19 demands novel solutions to boost healthcare services and economic growth. A full-fledged solution that can help the government and people retain their normal lifestyle and improve the economy is crucial. By bringing into the picture a unique incentive-based approach, the strain of government and the people can be greatly reduced. By providing incentives for actions such as voluntary testing, isolation, etc., the government can better plan strategies for fighting the situation while people in need can benefit from the incentive offered. This idea of combining strength to battle against the virus can bring out newer possibilities that can give an upper hand in this war. As the unpredictable future develops, sharing and maintaining COVID related data of every user could be the needed trigger to kick start the economy and blockchain paves the way for this solution with decentralization and immutability of data.

cs.CY↗

arXiv2020

Multiclass Model for Agriculture development using Multivariate Statistical method

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

cs.LG↗

arXiv2020

A Review on Cyber Crimes on the Internet of Things

Internet of Things (IoT) devices are rapidly becoming universal. The success of IoT cannot be ignored in the scenario today, along with its attacks and threats on IoT devices and facilities are also increasing day by day. Cyber attacks become a part of IoT and affecting the life and society of users, so steps must be taken to defend cyber seriously. Cybercrimes threaten the infrastructure of governments and businesses globally and can damage the users in innumerable ways. With the global cybercrime damages predicted to cost up to 6 trillion dollars annually on the global economy by cyber crime. Estimated of 328 Million Dollar annual losses with the cyber attacks in Australia itself. Various steps are taken to slow down these attacks but unfortunately not able to achieve success properly. Therefor secure IoT is the need of this time and understanding of attacks and threats in IoT structure should be studied. The reasons for cyber-attacks can be Countries having week cyber securities, Cybercriminals use new technologies to attack, Cybercrime is possible with services and other business schemes. MSP (Managed Service Providers) face different difficulties in fighting with Cyber-crime. They have to ensure that security of the customer as well as their security in terms of their servers, devices, and systems. Hence, they must use effective, fast, and easily usable antivirus and antimalware tools.

cs.CY↗

arXiv2020

The Adoption of ICT Powered Healthcare Technologies towards Managing Global Pandemics

Pandemic is an outbreak that happens over a large geographic area affecting a greater portion of the population as new pathogens appear for which people have less immune and no vaccines are available. It can spread from person to person in a very short time, and in fact, the health workers are at greater risk of infection because of the patients who carry the disease. In the 21st century, where everyone is connected through digital technologies, Information and Communication Technology (ICT) plays a critical role in improving health care for individuals and larger communities. ICT has currently been severed in a variety of application domains which signifies its importance as a major technological paradigm, and it has drawn higher attention for its potential to alleviate the burden on healthcare systems caused by a rise in chronic diseases, aging and increased population and pandemic situations. This paper surveys and offers substantial knowledge about how effective ICT Healthcare strategy can be used to manage global pandemics by presenting a four-phased framework, which can be deployed to alleviate the strain on healthcare during a pandemic. In addition, we discuss how ICT powered technologies can be used towards managing a pandemic during the transformation of simple disease outbreak into a global pandemic.

cs.CY↗

arXiv2020

A Review on Security and Privacy of Internet of Medical Things

The Internet of Medical Things (IoMT) are increasing the accuracy, reliability, and the production capability of electronic devices by playing a very important part in the industry of healthcare. The available medical resources and services related to healthcare are working to get an interconnection with each other by the digital healthcare system by the contribution of the researchers. Sensors, wearable devices, medical devices, and clinical devices are all connected to form an ecosystem of the Internet of Medical Things. The different applications of healthcare are enabled by the Internet of Medical Things to reduce the healthcare costs, to attend the medical responses on time and it also helps in increasing the quality of the medical treatment. The healthcare industry is transformed by the Internet of Medical Things as it delivers targeted and personalized medical care and it also seamlessly enables the communication of medical data. Devices used in the medical field and their application are connected to the system of healthcare of Information technology with the help of the digital world.

cs.CY↗