Proceedings of International Conference on Network Security and Blockchain Technology

Today’s healthcare system is largely dependent on information technology. Cloud-based patient data storage and retrieval is quickly gaining popularity in the medical world. However, it has been observed that the cloud platform is vulnerable to different types of attacks, the most common of which is distributed denial-of-service (DDoS) attack. This attack is capable of halting the network within a very short time and prevents access to important data. Among the different types of DDoS attacks TCP-SYN FLOOD attack renders the servers useless in no time by sending multiple malicious packets. It is necessary to identify such attacks at the onset. In the proposed work the authors tried to distinguish between the normal traffic and TCP-SYN FLOOD attack within first 1 min of the transmission. Different machine learning algorithms such as multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) were used to construct the model to classify the attack. Statistical metrics were used to assess the performance of the models. MLP was found to have the highest accuracy of 99.32%. A comparison was made with similar previous works, and some future research prospects were also discussed.

At present time phishing attack is one of the biggest problem in our regular life. Phishing email is a type of cyberattack, which is a serious problem for financial damage in commercial organization. In phishing email used a special trick to the user that can access his digital assets. Generally phishing attack targeted to normal users by sending email that contain malicious links which are used to spoofed websites, by which the attacker can collect different sensitive information. This paper proposes a machine learning base comparison approach using persuasion principle and Natural Language Processing (NLP) base features (TF-IDF). Here we use 7 different classifiers for phishing email detection. We prepared a emails dataset collected from the Enron corpus and the Nazario phishing email corpus which are well-known and the most popular. We performed the comparative result analysis of different classification algorithms and shown the heighst performance with 99% accuracy value of AdaBoost classifier for phishing email detection.

Smart grid (Sg) is an enhanced electrical power grid that helps regulate power distribution and communication among the users and the service providers. The upsurged growth of communication technologies intensifies the monitoring abilities of Sg by utilizing its bidirectional communication properties, which further contribute to meet users’ real-time demands. Smart grid technology is reliable and highly efficient. However, user-server bidirectional communication shouldn’t be exposed to various cyberattacks. Recently, a mutual authentication scheme for smart grid communications has been devised by Khan et al. (J King Saud Univ Comput Inform Sci, 2019). Unfortunately, we have found some design flaws, such as lack of mutual authentication between the user-server and no session key agreement in the proposed scheme. Also, the scheme does not withstand offline password-guessing attacks, user impersonation attacks, and replay attacks. Thus, to alleviate the existing issues, we have devised an improved authentication scheme. Finally, we demonstrate that our proposed scheme is secure against all possible types of security attacks by performing an informal security analysis.

One of the biggest issues confronting metropolitan cities today is the sharp rise in traffic accidents. This is a result of an increase in collisions caused by the use of motorbikes and cars. Driving too quickly is also dangerous. The lack of cutting-edge techniques to reliably identify and stop accidents is another issue; accidents are strictly managed. A realistic, practical, and agreeable strategy needs to be put in place if the country is to experience a decrease in the number of accidents. It is more likely that someone will pass away due to the mere fact that the ambulance takes a longer time to arrive at the site of the accident and for medical professionals to evaluate the victim’s condition. The proposed method was able to recognise accidents and track the car in a quicker and easier fashion. It ensures that the victim receives early medical attention, and the time needed to secure the resources needed after an accident is considerably reduced. The device serves as an accident identification system by gathering data on the affected vehicles and transmitting it to the nearby control centre.

The advantages of digital data are spreading throughout all industries in the modern world, and securing data while exchanging is extremely significant. In this study, we have devised a new encryption and decryption technique, which is integrated with graph labeling for the conversion of cipher text. Our algorithm is based on AUM block sum labeling on any block graph and incorporating perfect square number. An illustration of the new technique for transmission is shown.

A novel and efficient 2-level secure reversible data hiding (RDH) method has been proposed based on image interpolation and Arnold transformation. An input image is scaled-down to original image and then scaled up to cover image using NMI interpolation method. The gray values of pixels are classified into a set of ranges. A difference value is calculated from adjacent pixels and it is used to find out the lower bound and upper bound of the range interval to which it belongs to. Before embedding, the secret data is scrambled using Arnold transformation implementing the 1st level security of proposed method. The scrambled secret data again updated to modified secret message to implement 2nd level security of proposed scheme. The input image is taken as reference during data embedding phase to raise the quality of the stego image. The experimental analysis shows that the proposed method has relatively higher imperceptibility and data embedding capacity than other schemes.

The whole world was suffering from the COVID-19 pandemic since 2019, which originated in the city of Wuhan, China and has quickly spread various countries, with many cases having been reported worldwide. Around 31.3crores cases had been recorded worldwide, among which India has been recorded 3.59crore cases according to recent data. Each time, this virus comes into existence with a new variant or strain. Recent studies have shown that through proper use of masks this kind of viruses can be prevented from spreading. Every nation is attempting to stop the disease's spread. Each individual must put on a mask in a public area in order to remedy the issue. Therefore, using a convolutional neural network, we propose a model that can distinguish between masked and non-masked faces. This investigation approaches to check whether a person is wearing a proper mask or not, and the mask is secure or not towards these viruses. This system mainly can be used in different transport system such as public bus, trains, aeroplane, etc., where bus conductors, ticket checker in trains and air hostage, respectively, did not had to go to near the passengers to ask them for using a proper mask. Based on CNN and MobileNetV2, a deep learning (DL) framework, this model is capable of identifying individuals without masks. It is accurate to a maximum of 99.76% using MobileNetV2 and 99.83% through CNN approach. A model has been compared with state-of-the-art methods which will be used to track the use of masks in locations including schools, offices, and other public spaces.

The onset of COVID-19 has shifted people to the virtual world more to avoid social interaction. In fact, the trading systems have shifted to E-Commerce platforms which include virtual transactions as well. Besides being a boon, virtual transactions have also brought in several cybercrimes. Several fraudulent sites are created for distracting the users from authentic sites and lead them to the unsafe ones where users lose their personal data or even their wealth at worst cases. This paper is to illustrate a machine learning solution for putting a check on these kinds of activities. Different Python tools have been used to preprocess the dataset that contains different URLs which is classified into safe unsafe categories depending on the presence of SSL certificate or encryption of the URL which is further fed into a model that classifies URLs into safe and unsafe categories using logistic regression. Other than earlier approaches, our proposed model not only checks simple URLs but also check whether URLs are safe for transaction.

This work uses the fuzzy weight strategy to construct a novel image interpolation method. By taking into account the fuzzy weight value of each pair of pixels in a chosen block, the interpolated pixel values are created. Each input pixel pair’s fuzzy membership values have been taken to represent the range between the block’s minimum and maximum value. The input membership value is fed into the fuzzy output function, which calculates the fuzzy rule’s strength using the Max–Min composite principle. Then, through a defuzzification process, interpolated pixel values are calculated from the fuzzy output function dependent on the strength of the fuzzy rule. In actuality, fuzzy weight based interpolation algorithms create virtual pixels, which are superior to the interpolation techniques now in use. The results of the experiments show that the suggested method almost always produced images with the highest PSNR. So, in the suggested technique, the FWS was used to generate the improved cover image.

In this research, a high capacity reversible data concealing strategy has been developed for secure data transfer utilizing multi-level pixel value ordering (MPVO) and center-folding strategy. Center-folding strategy was first used to insert hidden information into an interpolated cover picture, creating dual marked images (DMI). Subsequently, multi-level pixel value ordering uses to incorporate more hidden information on dual marked images. The center-folding approach has been used in this case because it offers the benefit of compressing important hidden information via averaging. By using neighboring and reference pixels, MPVO, on the other hand, offers the ability to embed an image block with more than two pixels. The secret data’s changing size and the varying picture block sizes required for effective embedding make the suggested technique very adaptable. In comparison with the previous state-of-the-art approaches, the experimental results reveal that the suggested method is particularly suited for embedding more concealed data without degrading the visual quality of the picture. The expected outcome brought to light several outstanding capabilities in the fields of covert data transmission, tamper detection, and digital forgery detection, without which the development of technology would be severely hindered. This program greatly benefits several aspects of the public and corporate sectors, such as health care, business security, defense, and rights to intellectual property.

Lossless audio encoding is a crucial technique by reducing the size of an audio file while maintaining privacy and perseverance of the audio data. In the present paper, a lossless audio encoder has been developed using a matrix segmentation method based on recurrent neural networks and the RLE methodology. Experimental data are shown with statistical metrics to assess the quality and efficiency of the current method in comparison to other existing algorithms.

In the current pandemic COVID-19 situation, many patient records are exposed without their knowledge, so, a novel technique is needed to protect the authenticity of patient information by incorporating a new watermark to the medical images. So checking the authenticity of any digital copy becomes very much important aspects of digital communications. In this proposed work, a novel SVD based watermarking method has been introduced for medical image authentication in various medical applications. This method examined against various types of threads and attacks. The results, show that the proposed scheme achieved 73.36 dB PSNR and SSIM is near about 0.99 with 64,980 bit embedding capacity. Compared to the traditional methods, proposed method effectively increases the accuracy of tamper localization as well as PSNR value of the self-recovered image are both significantly increased.

Image-based watermarking has become essential in the digital authentication system. This work uses coefficient value differencing and histogram shifting techniques for watermark embedding on the coefficients of integer wavelet transform to take advantages of both spatial and transform techniques. After some necessary preprocessing, the cover image is applied 3/1 integer wavelet transform (IWT) to get four sub-bands. Thereafter, SHA256 algorithm is executed on the watermark image to generate hash code for authentication. The watermark and the hash code are embedded in the HH and HH of the LH sub-band, respectively, using coefficient value differencing and histogram shifting techniques. In the authentication phase, the stego-image is processed to extract the watermark and SHA256 code, followed by computation of SHA256 code from extracted watermark. These two SHA256 codes are compared to establish authentication. The experimental results on standard dataset show average “mean square error” (MSE), “peak signal to noise ratio” (PSNR), and “structural similarity index measure” (SSIM) between the cover and stego-image to be around 0.10, 60 dB, and 0.999, respectively. The robustness of the system is verified with well know active attacks by computing normalized cross-correlation (NCC).

This paper deals with an updated SNOW 3G key stream generator (KSG). The proposed KSG can be applied to encrypt images in the Internet of Things (IoT) applications. The KSG algorithm replaces the LFSR structure of SNOW 3G by a $$\sigma -$$ LFSR of delay blocks of $$64-$$ bit input–output and 8 gain matrices. It also incorporates a new feedback polynomial with 8, $$64 \times 64$$ coefficient matrices over GF(2). As a nonlinear component, 16, 8 input-8 output parallel S-BOXes are concatenated to produce the 64 input and 64 output S-BOXes S1 and S2. The algorithm generates a 64 bit pseudorandom number in each clock pulse, making the image encryption faster than some existing techniques. We practically justify the algorithms with various security parameter analyses.

Algorand is a new cryptocurrency confirming transactions in a few minutes and scales to a large number of users. Even if some users are bad and the network is momentarily partitioned, Algorand assures that users never have contradictory views of confirmed transactions. Contrarily, current cryptocurrencies support momentary forks and demand a long time to confirm transactions with great confidence. Algorand uses a unique Byzantine Agreement (BA) technique based on Verifiable Random Functions (VRFs) to scale consensus to many users in order to reach an agreement among users on the upcoming round of transactions. In order to agree on the following set of transactions, this technique enables users to secretly verify if they have been chosen to engage in the BA and to include evidence of their selection in the network messages. In BA used in Algorand, other than their private keys, no other private values are stored, so users can be instantaneously replaced after sending the message. This protects selected individuals from targeted attacks after their identities are known and also helps to detect deepfakes. Experimental results have proven that the confirmed transactions are achieved within a minute and detect deepfake users in minimum time.

As information technology has become more ingrained in people’s lives, data protection has become more and more crucial. On the other hand, malicious programs are being created that could tamper with sensitive and important information and restrict the access to it. A perfect example for such is ransomware, it locks down a computer and prevents users from using it until a ransom is paid. Every 11–14 s, a brand-new organization gets assaulted. Faster recovery is facilitated by early ransomware detection. In this paper, to detect ransomware, several machine learning models are trained using information derived from portable executable (PE) file structure. The proposed approach classifies ransomware applications with 99.4% accuracy by using 14 features. These 14 features are important, and it is enough to provide the accurate classification result. And, to improve the efficiency, the classified file is further examined to check for any bitcoin addresses being present or not through YARA rules.

Data mining, with the aim of decrease the statistical calculations linked in the direction of the recurring appliance of the accessible integration procedure in calculating a huge amount of calculated principles, a procedure has been obtained commencing the numerical and integration procedure for indicating the statistical data on randomly omitted principles in list. The closest fit way In the case of indicating the facts about the emissions of carbon dioxide global from burning fossil fuels by petroleum Type associated as a technique, the formula to statistical information has been made public.

High-speed communication technology with the implementation of 5G and Wi-Fi-6 is now getting popular, and the usage of these technologies is growing fast. The Government of India has announced the fast paced implementation of 5G and then to look forward to 6G Technology. The vulnerability and potential attack possibilities grow as data transfer rises, and as more and more devices gets connected to the next generation network. Ability to employ this technology for mission-critical applications has been made possible by the Wi-Fi-6 and 5G network, making it crucial to have security solutions in place. In this paper, we have tried to focus on the most widely used attack, i.e., IoT-DDoS attack on the next generation high-speed network, with the technologies like SDN, these attack can devastate the network itself. Further, we discuss the possible threat that may use the smallest devices, i.e., IoT devices for the launch of DDoS attacks. Further, the paper discusses the possible mitigation for the security of these devices, networks, and resources.

Digital watermarking is commonly used to hide information for confidentiality and copyright protection and to provide data authentication of medical images. This paper represents an algorithm that uses double images, interpolation, and the SHA-512 method to protect digital documents from illegal access. This increases the ability of the watermarked image to hide and limits the alteration of the cover image. The algorithm depends on some significant steps: the image expansion, i.e., a procedure for converting to an image of high-resolution from a image of low-resolution, hiding secret data within the cover image, selection of one image from a dual image by applying a secret key, and selection of watermarked image using the secret key and finally extract the original cover image. This proposed technique is more authentic. The results of this paper define the quality of image of the used techniques outperform the earlier works.

Congestion control in TCP is a crucial task. There are many congestion control techniques exists. The aim of these methods is to prevent congestion and also to maximize the use of bandwidth available. The implementation of a single congestion control algorithm under varied environments of networks still remains a daunting task, as the performance improvement parameters differ for different TCP. This paper presents a novel approach to deal with congestion which requires communication between layers for improving estimation of congestion. The better the congestion estimation better will be the counter measures can be done.

The computer technology is being advanced day by day such that it results in being subjected to various data breaches and network attacks that might cause a huge damage to the data and the network. These network attacks cannot be ignored and must be prevented to maintain the at most security so that the integrity is maintained. Thus, the crucial job for the network administrators in the current generation is to choose better preventive measures so that the network is not vulnerable to the hackers. The Intrusion Detection Systems are one of the best solutions to prevent the further damage that can be caused by the hackers. We propose to develop a deep learning model using convolution neural network (CNN) to analyze the network traffic and classify the type of Bruteforce attack that has been performed. This method will be validated using a portion of CICIDS-2019 dataset. The model is proposed such that it is efficient enough to classify the network attack and have an improved accuracy when compared to other machine learning models.

When data travels through an insecure medium, security must be enforced. The confidentiality of the exchanged data must be guaranteed with the help of encryption techniques. Selective encryption is a very powerful tool for encrypting textual data in a resource constrained environment. In the context of e-governance, textual data is very important and must be protected from any kind of security threat using selective text encryption. In this paper, a fast and efficient selective encryption technique based on an RSA asymmetric key encoding approach is presented. After fetching the whole textual information in the encryption phase, the user will search for a particular word or phrase using regular expression. After that, the selected data will be encrypted using the RSA-1024-bit algorithm and written to a new encoded document with the remaining text data. In the phase of decoding the data, only the encoded text of the document is considered. The experimental result confirms that the encrypting method is secure in terms of statistical security tests and fast in terms of computation time. Our encoding method can be implemented to encrypt any multimedia data like images, audio, and videos. This proposed technique can be used in IOT devices where resources are limited.

Malicious URL distribution channels are used to host malicious data over the Internet. They are primarily responsible for transmitting spam, malware, adware, spoofing, inappropriate data, and resources. So victims get exploited for information disclosure, gaining unwanted access, financial loss, and extortion. Web applications are used by cyber attackers to gain remote access and covertly monitor sensitive data. Victims are driven by email, social media sites, or Web searches through malicious URLs. So, it gets a compromise. Traditional techniques like blacklisting, signature matching, and pattern matching are getting more complex due to the ever-growing volume of signatures, patterns, and features. Technology is changing over time and needs continuous innovation to sustain itself. In this paper, we propose a novel mechanism for classifying malicious URLs by comparing benign Web-based URLs with well-known classifiers, and our proposed procedure evaluates benign Web-based URLs while considering other malicious parameters as well.

Given the rapid increase in traffic on the current communications infrastructure, such as the Online World, it is imperative for consumers to understand how these systems are developed swiftly and successfully scheduled to their servers. This study focuses on how we can accomplish this using the queuing theory currently in use. This article also covers current and comprehensive information on the use of queuing techniques in fields including load balancing, mobility management, and improving traffic flows on the current Internet infrastructure. The problem is transformed into a non-Markovian mathematical queuing model, and auxiliary variables are used to solve it. The queuing issue that arises from the aforementioned subsequent outcomes is resolved by the supplemental variable technique. Estimates are made for the throughput, server latency, use, and probability output factors for each operating method. Mathematical software was used to do numerical analysis on specific examples. Because it is used frequently and uses a statistical demarcation method, this tactic is completely acceptable. Exact computations of the seeming limitations are provided by the graphical representation of this perspective.

Develop a graphical user interface tool for the diagnosis of diabetic retinopathy complications such cotton wool spots, haemorrhages, and exudates (EX). By employing digital image processing techniques and multi-resolution analysis techniques (Wavelet decomposition). In order to do a research on this technique, we have utilised local databases created by Dr. Manoj Saswade in addition to web databases such as DiarectDB0, DiarectDB, STARE, and DRIVE. In all, we have 1180 high resolution images of the fundus. The optic disc is removed from each and every fundus picture using preprocessing, which is the initial step in this approach. The following stage is to implement numerous algorithms for DIP, such as the intensity transformation, complement function, histogram equalisation, and threshold. Other algorithms to build include the histogram equalisation and threshold. Wavelet decomposition will be utilised within the symlet wavelet. Then the removal of the lesions and apply statistical methods. After that, analyse the performance of the proposed method by computing the sensitivity and specificity using a receiver operating characteristic curve. Using several statistical methods, we were able to obtain an overall result of 94%.

In cloud computing, users may access their crucial data that is kept on distant servers over the Internet. The amount of sensitive and important data is expanding quickly as technology develops every day. Data security is crucial in cloud computing because cloud computing involves storing and processing data on remote servers, often belonging to third-party providers. This means that data is transmitted and stored outside of the user’s immediate control, making it more vulnerable to security breaches and cyberattacks. A security breach of user data highlights the importance of user privacy. Every form of data has a varied level of protection requirements. In this paper, we have suggested a model that categorizes the data in accordance with its security characteristics. We have created a new hybrid approach to achieve this. We have built a hybrid algorithm using AES and DES. The performance of the hybrid system is compared to the current hybrid technology, showing that the recommended approach offers excellent security and confidentiality of user data. The shortcomings of both symmetric and asymmetric encryptions are solved by hybrid cryptography and steganography.

This article focuses on providing security of sensitive information and message transmission using symmetric key cryptosystem based on genetic algorithm and combined with mathematical and bitwise operators to provide confidentiality, authenticity, integrity, and nonrepudiation of the messages. A plain text is encrypted using a private key and resizer parameter taken as input from the user to produce an intermediate cipher which is further encrypted using genetic algorithm and mathematical operations to obtain the final cipher. The level of complexity of encryption and the size of the final cipher text can be regulated using the resizer parameter. The final cipher is decrypted first to obtain an intermediate cipher, which in turn is decrypted using the private key to get back the plain text.

Cloud computing is an environment that provides processing and executing requests in the form of tasks on the Virtual Machines (VMs). The resource scheduling and load-balancing mechanisms of the cloud are important to keep an easy flow of task processing and execution at any instance of time, irrespective of the task size. Therefore, it becomes crucial to study the behavior of resource scheduling algorithms with respect to the load-balancing mechanism. The main objective of this research paper is to process and execute different-sized tasks in the WorkflowSim environment on the cloud VMs by using the resource scheduling algorithms Max–Min (MX–MN), Minimum Completion Time (MCT), and Min–Min (MN–MN) under different scenarios with the purpose to study the behavior of these resource scheduling algorithms with respect to the load-balancing mechanism. A mathematical model is also proposed to calculate the amount of load balanced by a specific VM. The mathematical model of linear regression is used to provide an empirical analysis and differentiate how the resource scheduling algorithms behaved under these various scenarios. From the experiment, results obtained, and empirical analysis, it can be said that the performance of the MX–MN is best, followed by the performances of MN–MN and MCT, respectively. The machine learning (ML) method of reinforcement learning (RL) is also proposed at the end to enhance the resource scheduling and load-balancing processes of the cloud.

Bangladesh is a nation that mainly relies on agriculture for its resources. And any farm could experience a mishap that would result in irreparable losses and near bankruptcy for the owner. When livestock is injured or killed as a result of an accident or disease, farms, ranches, and other businesses that raise or care for livestock, such as livestock farms, can suffer financial losses. By purchasing farm insurance, farm owners can protect themselves against the loss of priceless animals and farm equipment. However, an effective structure is needed to ensure insurance which is easy to administer and tailored to farm owners, helping insurers propose appropriate insurance and identify fake insurance claims. In this study, we developed a system that assists insurance companies in conducting farm insurance, utilizing the hyperledger fabric-based blockchain in the safest way imaginable. We opted to use this technology due to its private nature, which offers access control and ensures the protection of sensitive data, which is the key priority for any insurance company. In an experimental test, our system outperformed Ethereum and Bitcoin with a scalability of 0.021 s for each transaction.

Foodborne disease is still a major public health concern in both advanced and emerging economies. In terms of adopting new technology, the health industry continues to lag behind all other sectors. India has traditionally relied on food farming to make money but which food item is good for whom we have no proper idea. Especially when it comes to healthcare sectors, the Internet of health things (IoHT) and blockchain can play an important role. It can enhance the quality of care for its users and reduce costs by allocating the correct food chain to the targeted users. We are currently employing blockchain in supply chain management to cluster the patients based on their health care type. For example, diabetic patients prefer a particular type of food. Segregating the food type within the supply chain can reduce a lot of effort and cost. Blockchain can provide a solution to the issues that arise in the healthcare-based supply chain system because of its decentralized and immutable nature. The service's execution has been depicted in pictures and accompanied by detailed descriptions. The ultimate result of this study was to demonstrate the immutability, availability, and transparency security of blockchain in the healthcare-based supply chain.

The rising of online media has delivered gigantic interest among Internet customers today. Data from these long reach relational correspondence regions can be used for different purposes, like assumption, promoting or assessment examination. Twitter is a comprehensively used online media site page for posting comments through short circumstances with. The enormous quantities of tweets got every year could be presented to notion examination. Nevertheless, managing an especially huge proportion of unorganized data is a dull work to take up. The analytics tools and models currently available on the market are not satisfactory for managing huge amounts of data. Therefore, we have used Hadoop for intelligent evaluation and bounding of huge data. In this proposed work, we have performed an evaluation of user feedback in a Hadoop environment.

This paper presents a system of IoT-based online attendance monitoring using fingerprint as biometric feature. It can eliminate fraudulent attendance problem faced by other attendance monitoring systems like RFID. Individual have to be present physically to register his or her attendance by giving own fingerprint impression. The system will also reduce problems such as keeping records in papers manually which can be lost or damaged easily. In the proposed method, firstly, fingerprints of the candidates have to be stored in a remote database through user interface using IoT. During verification, fingerprint sensor will check the impression of particular candidate with the pre-registered template, and the result will be shown in OLED display. Continuous assessment of attendance will be recorded in Web database which can be accessed by both candidate and admin. Leave management system works on request-approve method where the users can apply for pre-leave/post-leave of various types like EL, CL, ML, etc. Also, the system allows the admin to publish public notices that can be accessed even without login. Implementing this system in corporate will help the authority to keep track of the attendance of employees and to increase transparency to promote proficiency in work-culture.

Blockchain is growing rapidly and becoming popular to handle business needs over the globe. Smart contracts are one of the unique features of blockchain 2.0 which are disrupting the industry and creating new value for businesses that are looking for opportunities to grow in an untrusted environment. There are many blockchain platforms evolved for serving different purposes, but each has its own limitations and advantages. Due to the lack of performance analysis tools and a complete study comparing public and private blockchain platforms, the researchers are focusing more on analyzing performance and comparing results. Considering these aspects, there is a necessity to conduct a performance study and comparison of public and private blockchain platforms. Here in this work, the performance and scalability issues of blockchain platforms are presented and provided in detail study, future scope. This work compares the existing blockchain platforms’ performance and discusses their pros and cons, in terms of performance and scalability.

Internet of medical things (IoMT) has become one of the revolutionary technology nowadays due to wide spread use of IoT in healthcare applications. IoMT improves the human–machine interaction enhancing the instantaneous monitoring of patient’s health and also involves patients in making decisions. So, the current medical devices have to be transformed into IoMT-based medical devices to collect patient information instantaneously. The patient’s health-related information can be observed from remote location without any need to go to hospital, and this data can be processed and is sent to mediators for further use. So, we are in need of technology that secures the confidentiality of the health information of patients. Blockchain is one such technology that helps in securing privacy of patient’s health-related data. Blockchain technique uses a sequence of blocks comprising the block ID, previous block ID, and the transactions. The preceding block ID can be used to maintain a link between subsequent blocks. These links continue until the first block is reached. This ID contains hash in blocks, and this hash is appended to subsequent blocks in chain. Any modifications to the patient information in a block will change the subsequent blocks in the chain also. So, changes to the data made by a hacker will become impossible. It uses public key cryptography for performing transactions between nodes. So, use of such type of cryptographic hash technique and chain formation for the blocks guarantees the security and privacy of the patient’s health information or records.

Blockchain is a public open ledger that provides data integrity in a distributed manner. It is the underlying technology of cryptocurrencies and an increasing number of related applications, such as smart contracts. Secure covert communication can be implemented as a combination of cryptography and steganography. Cryptography ensures that the communicated message stays private, while steganography is used to hide the fact that the encrypted communication provide. The key idea of this investigation is to automate the covert communication on blockchain technology. Covert communication channels are designed to protect the relationship between the transmitter and the receiver by hiding the fact that secret communication is taking place. The proposed scheme provides higher embedding capacities compared to the existing schemes. The motivation behind the development of this research work is an attempt which create a practical application that will help in improving the emerging use of IT technologies in a developing country like India today.

An innovative and common way to raise funds for tasks is to crowdfund or crowdsource. People can use the acquired fundings to create projects that contribute to the betterment of the society. Current crowdfunding platforms charge a significant fee for every event or project that is listed. The fee charged is sometimes a fixed amount or a percentage of the total donation required. Centralized crowdfunding platforms do not bother to protect intellectual property either. It is possible that the start-up idea of a smaller group could get stolen by a bigger company. This creates pressure on smaller groups to meet deadlines and spend the donation money to patent their projects. Our platform will be a one-stop solution for all funding requirements such as start-ups, medical needs, charities, and so on. It will be secure, transparent, and easy-to-use.

The purpose of this research is to understand the possible linkage between variables that constitute blockchain technology adoption in small and medium enterprises (SMEs). In the study, the authors tried to decode the complex relationship among variables which is missing in extant literature. The authors employed interpretative structural modeling (ISM) to detect the complicated equations among variables after identifying the variables through a review of the literature. The ISM model helps the industrialist to understand the various process involved in the adoption of blockchain technology (BCT) in SMEs. The results of the study identified that a three-level approach is needed for the adoption of BCT in SMEs. First, the company needs to have a sophisticated usage of information technology. IT project management, innovation complexity, benefit compared with other technological choices, compatibility with the organization, and trialability are the level 2 variables in which the small-scale industries have to focus before the implementation of BCT. Once the company is equipped with first and second-level variables, SME should focus on variables such as the implementation cost of BCT, software revision of BCT, the maintenance cost of BCT, and observability.

E-coupons are used often, as e-commerce becomes increasingly popular because of its mobility and ease of use. The majority of e-coupon providers manage e-coupon data on a single and centralized server, which makes them susceptible to security problems. To address this issue, a novel system is proposed to provide e-coupon service with enhanced security by employing blockchain technology and the HMAC Digital Signature Algorithm. Additionally, we improve the blockchain performance (in terms of speed and security) with a coupon recommendation contract and a malicious block avoiding contract. The experimental findings demonstrate that, when compared to an existing e-coupon service, stronger security is provided by our proposed service.

A credit score represents an individual’s financial ability and stability which is calculated upon various factors like financial behavior of transacting, re-paying loan on time, spending in a healthy manner, etc. Approval for loans, credit score is strictly restricted for a set of people who have a strong economic support and perfect access to every transaction. But in a region with a major population that doesn’t even have access to digital transactions, this is a way to make them financially stable. By collecting various information about their financial history and setting up an application that can track financial behavior and store every transaction possible in blockchain, we can solve this problem. Blockchain-based ledger application is used to calculate credit score for non-registered people who cannot access credit score until now. This is more secure to store every transaction made by the user and analyze the behavior to generate a credit score. So that they can show it as proof for taking credit for their personal development and uses. Anyone who has an ability to take credit must not be wasted, because when there is a healthy flow of money, the healthier would be the country’s economy.

Building models for improved decision-making using mobile, IoT, social media, cloud, and other technologies is driving higher efficiency, digital products, and deeper consumer relationships in the digital era. During negotiation and execution, the terms of an underlying legal contract are digitally facilitated, verified, or enforced through smart contracts, a sort of electronic transaction protocol to meet typical contractual requirements. In order to lower transaction costs, including arbitration and enforcement expenses, this article intends to create an unusual intelligent contract architecture. In this case, it is done by implementing trackable and irreversible transactions using blockchain technology for distributed databases. By fostering the entrepreneurial collaboration of cross-organizational business activities typical of intelligent supply chains, ‘CRYPTOLIGATION’ has the potential to go much beyond cost savings. This approach concerns that are covered in this article include how and to what degree blockchain technology and smart contracts might help design cooperative transaction structures for long-term P2P activities in intelligent supply chains. The goal of ‘CRYPTOLIGATION’ is to look at the challenges, difficulties, and advantages and by using blockchain technology in many fields of the present research work. The obstacles to smart contracts in a real-world setting in future are discussed in this paper's conclusion.

The healthcare industry is ripe for some major changes. From chronic diseases and cancer to radiology and risk assessment, there are nearly endless opportunities to leverage technology to deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care. As technology advances, patients demand more from their doctors, and the volume of available data continues to increase at a staggering rate, artificial intelligence is poised to be the engine that drives improvements across the care continuum. No cure exists for dementia, but promising drugs have emerged in recent years that can help stem the condition’s progression. However, these treatments must be administered early in the course of the disease to do any good. This race against the clock has inspired me to search for ways to diagnose the condition earlier. This paper aims to elaborate on AI-produced smart application using machine learning that aims to aid dementia patients in identifying symptoms that highlight significant decline of cognitive functions so that they can be diagnosed and help the patient from further deterioration of dementia disease. There is often lack of awareness and understanding of Alzheimer’s which leads to delay in diagnosis and care. Thus, there is need to spread awareness which will help the community to know about this disease and can get checked for it instantly via model. Model designed achieves an accuracy of 84% which is at par with existing models.

Education being an indispensable to our lives, efforts are continuously being made to strengthen it. Assessment lies at the core of any given educational program or module, irrespective of modes (online, offline, blended) and format (short time, part time, or full time). Among the various types of assessment, continuous assessment has been widely adopted. Quizzes are one of the important and common ways to conduct continuous assessment. However, due to the existing processes and involved centralized technologies, there are numerous challenges such as non-transparent and insecure conduction of assessment, non-verifiability of assessment process and computation of final grades from these assessments, and trusted sharing of assessment (or learning) logs along with final grades. Blockchain and smart contracts have emerged as potential technologies which can be leveraged to overcome these challenges. Thus, this paper proposes a blockchain-based architecture for conduction of online quiz. In particular, the paper aims to design and develop secure, transparent, and verifiable, Blockchain and Flutter-based Quiz Mobile-Decentralized Application (BFQM-DApp). To the best of our knowledge, this is first work which attempts to develop a blockchain and flutter-based mobile DApp for online quiz.

The nodes, or mobile devices, in a dynamic network are continuously moving and are part of a temporary network called the mobile ad hoc Network (MANET). Due to the open network, any node can join or leave the network at any time. So, security is one of the major challenges in MANET. The novel blockchain method is more popular for securing the network from attacks. The attacker nodes are the main issue in secure routing, which disgraces actual routing performance. In this paper, we propose the Blockchain Security Scheme for Blackhole Attack (BSSB) to prevent network from a single as well as multiple blackhole attacks in MANET. The proposed BSSP scheme prevents malicious communication and focuses on gathering malicious node information that is being used to emphasize the intrusion detection and prevention. Routing performance is evaluated in the presence of blackhole attacks, the previous MRREP, and the proposed BSSB approach in MANET. The blockchain method generates a separate block code for every change during routing. In this research, we are not focusing on only single node detection. The proposed scheme can handle the multiple malicious nodes present in the network. The BSSB approach generates a new hash code for every normal change during routing, i.e., for normal packet receiving and forwarding. The performance of the proposed scheme is compared with the MRREP scheme, and the BSSB is showing a better performance in network.

Agriculture is an important element of many societies and has played a key role in the development of civilizations. Agriculture is the cultivation of plants, animals, and other life forms for food, fiber, biofuel, medicinal plants, and other products used to sustain human life. Agriculture needs a considerable amount of cash investment on top of the effort and time of farmers. These days it is quite observable that many farmers face economic challenges, such as low crop prices, high input costs, and limited access to credit, making it difficult for them to sustain their operations. In this article, a blockchain-based solution has been proposed to meet up the gap between farmers and investors. In this article, the problems of farmers have been identified, the utility of blockchain-based solutions has been explained, and the architecture of the proposed solution has been demonstrated for finding investors, disbursement of investment, and repayment solutions.