BITS Faculty Publications
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Item Enabling Safe ITS: EEG-Based Microsleep Detection in VANETs(IEEE, 2022-12) Chamola, VinayResearchers nowadays are particularly focusing on the interpretation of EEG signals to understand and exploit the information they provide for brain activities. Deep learning architectures performing sleep staging have recently grown to their full potential with their ability to learn and interpret highly complex mathematical contexts. This has been catered to owing to the increasing availability of large EEG data sets. In this paper, we describe how sleep staging differs from microsleep prediction. We also provide a fresh methodology for the microsleep classification job that works with even less training data. Our proposed model exploits the attention-based mechanism that clubs the advantages available in Wavelet transform with Short Time Fourier Transform(STFT) Spectrogram. We also put forward a robust deep learning model that contains separate “time-dependent” and “time-independent” parts, which can record contexts from the sequence of features and simultaneously learn intra-epoch relations. A single-electrode EEG signal was employed for our analysis to accommodate such procedures’ social acceptance. For the task of microsleep detection on the MWT dataset, our model achieves fairly high accuracy rates (92% training and 89.9% testing accuracy), and an overall improvement in the kappa value by ≈ 42%, as compared to prior novel approaches.Item A Blockchain and ML-Based Framework for Fast and Cost-Effective Health Insurance Industry Operations(IEEE, 2022) Chamola, VinayHealth insurance is crucial for each person, bearing in mind the increasing medical costs. COVID-19 has been an eye-opener as to how important it is to have health insurance. Medical emergencies can have a severe emotional and financial impact. Thus, a health insurance policy can help mitigate financial risks in unpredictable circumstances. However, the current insurance system is very expensive, as thousands of people pay the premiums, and very few take the claims. Furthermore, the claim settlement process is excruciatingly long and tiresome. In this article, we focus on establishing a rapid and cost-effective framework for the health insurance market, based on machine learning and blockchain technology. By developing a smart contract, blockchain may eliminate any third-party organizations and make the complete process safer, easier, and more efficient. The contract pays the claim based on the claimant’s documentation. We optimized the premiums using a regression model based on the net amount claimed during the current policy tenure and various other criteria. For anticipating risk, a random forest classifier is used, which aids in the risk-rated premium rebate computation for policyholders for their next term of insurance.Item Uniting cyber security and machine learning: Advantages, challenges and future research(Elsevier, 2022-09) Chamola, VinayMachine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the implementation of some systems that can learn from the historical data, identify patterns and make logical decisions with little to no human interventions. Cyber security is the practice of protecting digital systems, such as computers, servers, mobile devices, networks and associated data from malicious attacks. Uniting cyber security and ML has two major aspects, namely accounting for cyber security where the machine learning is applied, and the use of machine learning for enabling cyber security. This uniting can help us in various ways, like it provides enhanced security to the machine learning models, improves the performance of the cyber security methods, and supports effective detection of zero day attacks with less human intervention. In this survey paper, we discuss about two different concepts by uniting cyber security and ML. We also discuss the advantages, issues and challenges of uniting cyber security and ML. Furthermore, we discuss the various attacks and provide a comprehensive comparative study of various techniques in two different considered categories. Finally, we provide some future research directions.Item Enabling Cost-Effective and Secure Minor Medical Teleconsultation Using Artificial Intelligence and Blockchain(IEEE, 2022-03) Chamola, VinayWhile the onset of the COVID-19 pandemic has increased the popularity of home-based consultations, worries over privacy, high consultations costs, slow response times, and the burden on doctors due to the overwhelming number of COVID-19 cases have made current in-person and online models ineffective. In this study, we present an advanced, privacy-protected, artificial intelligence and blockchain-based consultation framework for minor medical conditions. Patients can post their medical queries anonymously on the blockchain network, which may be answered by any available medical professionals. The queries are sorted into their respective domains using naive Bayes and logistic regression. The consultations provided by medical specialists are evaluated based on their reputation, expertise, detail orientation, and the use of supporting documents, and rewards are given in accordance with the evaluation scheme. This fair and incentivized system provides cheaper and more accessible healthcare to patients, which is the need of the hour.Item A machine learning and blockchain based secure and cost-effective framework for minor medical consultations(Elsevier, 2022-09) Chamola, VinayWith the ever-increasing awareness among people regarding their health, visiting a doctor has become quite common. However, with the onset of the COVID-19 pandemic, home-based consultations are gaining popularity. Nevertheless, the worries over privacy and the lack of willingness to assist patients by the medical professionals in the online consultation process have made current models ineffective. In this paper, we present an advanced protected blockchain-based consultation model for minor medical conditions. Our model not only ensures users’ privacy but by incorporating a calculation model, it also offers an opportunity for consulting end-users to voluntarily take part in the consultation process. Our work proposes a smart contract based on machine learning to be implemented for the prediction of a score of a professional who consults based on various prioritized parameters. This is done by using word2vec and TF-IDF weighting to classify the question and cosine similarity scores for detailed orientation analysis. Based on this score, the patient is charged, and simultaneously, the responder is awarded ether. An incentivized method leads to more accessible healthcare while reducing the cost itself.Item Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare(IEEE, 2022-11) Chamola, VinayThe COVID-19 pandemic has revealed several limitations of existing healthcare systems. Thus, there is a surge in healthcare innovation and new business models using computer-mediated virtual environments to provide an alternative healthcare system. Today, digital transformation is not limited to virtual communication alone but encompasses digitalizing the network of social connections in the healthcare industry using metaverse technology. The metaverse is a universal and immersive virtual world facilitated by virtual reality (VR) and augmented reality (AR). This paper presents the first effort to offer a comprehensive survey that examines the latest metaverse developments in the healthcare industry, which covers seven domains: telemedicine, clinical care, education, mental health, physical fitness, veterinary, and pharmaceuticals. We review metaverse applications and deeply discuss technical issues and available solutions in each domain that can help develop a self-sustaining, persistent, and future-proof solution for medical healthcare systems. Finally, we highlight the challenges that must be tackled before fully embracing the metaverse for the healthcare industry.Item Machine-Learning-Assisted Security and Privacy Provisioning for Edge Computing: A Survey(IEEE, 2021-07) Chamola, VinayEdge computing (EC), is a technological game changer that has the ability to connect millions of sensors and provide services at the device end. The broad vision of EC integrates storage, processing, monitoring, and control of operations in the Edge of the network. Though EC provides end-to-end connectivity, speeds up operation, and reduces latency of data transfer, security is a major concern. The tremendous growth in the number of Edge Devices and the amount of sensitive information generated at the device and the cloud creates a broad surface of attack and therefore, the need to secure the static and mobile data is imperative. This article is a comprehensive survey that describes the security and privacy issues in various layers of the EC architecture that result from the networking of heterogeneous devices. Second, it discusses the wide range of machine learning and deep learning algorithms that are applied in EC use cases. Following this, this article broadly details the different types of attacks that the Edge network confronts, and the intrusion detection systems and the corresponding machine learning algorithms that overcome these security and privacy concerns. The details of machine learning and deep learning techniques for EC security are tabulated. Finally, the open issues in securing Edge networks and future research directions are provided.Item Role of machine learning and deep learning in securing 5G-driven industrial IoT applications(Elsevier, 2021-12) Chamola, Vinay; Gupta, ShashankThe Internet of Things (IoT) connects millions of computing devices and has set a stage for future technology where industrial use cases like smart cities and smart houses will operate with minimal human intervention. IoT’s cross-domain amalgamations with emergent technologies like 5G and blockchain affects human life. Hence, increase in reliance over IoT necessitates focus on its privacy and security concerns. Implementing security through encryption, authentication, access control and communication security is the need of the hour. These needs can be best catered with the use of machine learning (ML) and deep learning (DL) that can help in realizing secure intelligent systems. In this work, the authors present a comprehensive review for securing Industrial-IoT (I-IoT) devices to contribute to the development of security methods for I-IoT deployed over 5G and blockchain. The survey provides a general analysis of the state-of-the-art security implementation and further assesses the product life cycle of IoT devices. The authors present numerous virtues as well as faults in the machine learning and deep learning algorithms deployed over the fog architecture in context with the security solutions. The potential security algorithms can help overcome many challenges in the IoT security and pave way for implementation with emerging technologies like 5G, blockchain, edge computing, fog computing and their use cases for creating smart environments.Item Smart Stock Exchange Market: A Secure Predictive Decentralized Model(IEEE, 2019) Chamola, VinayStock exchanges around the world are exploring the best possible solution that can improve trading efficiency, lower the risks and tighten secu- rity levels. The working and functioning of a stock exchange involves very hectic and cumbersome pro- cedures which are time consuming, cost inefficient and can be prone to numerous risks. Machine learning and Blockchain are most popular upcoming technologies. In this paper we present a novel secure and de- centralized intelligent stock market prediction model. We present a blockchain based solution for stock exchange model that uses machine learning accessible smart contracts. The machine learning model makes a prediction on the future of the stock market providing an intelligent solution for secure stock market.Item Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey(IEEE, 2021-02) Chamola, VinayDrone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.