Quantum Key Distribution (QKD) protocols, such as BB84, require secure authentication of their classical communication channel to ensure message integrity and authenticity, which is fundamental to successfully distribute unconditionally secure keys and prevent critical vulnerabilities. Traditionally, QKD relies on pre-shared secret keys for this authentication, posing significant scalability challenges. Employing asymmetric encryption methods is also not a viable alternative, as these methods are either vulnerable to quantum computing attacks or lack rigorous security proofs. To address these issues, we propose using the well-established Kerberos authentication protocol, which relies on symmetric cryptography inherently resistant to known quantum attacks, to securely distribute symmetric session keys that authenticate classical communication in QKD systems. We demonstrate the feasibility and practical security advantages of employing Kerberos-generated session keys, inherently resistant to quantum computing attacks, without modifying existing Kerberos workflows, providing a practical quantum-secure solution that leverages existing IT infrastructure.

Cryptocurrencies have gained immense significance and popularity in recent times. With thousands of digital currencies available, selecting the right one can be challenging for users. In the financial sector, accurately predicting future prices is crucial for profitable investments in digital currencies. However, price prediction in this realm poses unique challenges, as it lacks physical goods or services as the basis, unlike stock prices. Machine learning emerges as a pivotal tool for addressing this challenge and plays a vital role in price prediction. This research analyzes five prominent currencies - Monero, Bitcoin, Ethereum, IOTA, and Zcash - employing five models: SVR, LRG, Huber, RANSAC, MLP, and AdaBoost. The experimental results demonstrate promising outcomes, showcasing the ability to predict digital currency prices with an impressive R2 score of 1.0 for specific machine learning algorithms. This advancement opens new avenues for informed decision-making and profitable ventures in the dynamic world of digital currencies

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Several emerging areas, such as sensor networks, the Internet of Things (IoT), and distributed networks are gaining traction where resource-constrained devices communicate by sharing privacy-preserving information. Due to heavy cryptographic components, standard cryptographic algorithms do not fit these IoT devices. In this article, we propose an efficient zero-knowledge blockchain-based privacy-preserving decentralized healthcare finance system that is suitable for lightweight computer devices. The proposed design mainly focuses on noninteractive zero-knowledge proof, which substantially reduces the cost of communication between two devices. We explain the system framework and its use case for a healthcare financial system at a micro-level. However, it can also be extended easily to more general financial systems. Our system framework is efficient and lightweight, using more efficient zero-knowledge-based proofs; validation of the transactions is done in milliseconds. As an advancement to our work, the proposed healthcare financial system for lightweight computer devices is also auditable without leaking any extra information than required.

The majority of financial transactions are now conducted virtually around the world. The widespread use of credit cards and online transactions encourages fraudulent activity. Thus, one of the most demanding real-world challenges is fraud detection. Unbalanced datasets, in which there are a disproportionately high number of non-fraud samples compared to incidents of fraud, are one of the key obstacles to effective fraud detection. A further factor complicating the learning process for cutting-edge machine learning classifiers is how quickly fraud behaviour changes. Thus, in this study, we suggest an efficient fraud detection methodology. We propose a unique nonlinear embedded clustering to resolve imbalances in the dataset, followed by a Deep Belief Network for detecting fraudulent transactions. The proposed model achieved an accuracy of 94% with a 70:30 ratio of training-validation dataset.

With the ever-increasing reach of the internet, and its increasing access through various types of devices, the spread of malware, phishing attempts, etc. have steadily been increasing, along with their level of sophistication. Thus it becomes very important to conduct research on different methods to prevent such harmful attacks on systems and users. Using a malicious URL is the common way for hackers to attack a system, thus, to accommodate the variety attack vectors of malicious websites, 21 features were extracted from 651,191 URLs to train the proposed model. A two-stage stacked ensemble learning model, based on gradient boosting methods and random forest, has been trained and tested in the 70:30 ratio of the 651,191 URLs, and an accuracy of 97% has been achieved. Then Explainable AI (XAI) has been used to clearly explain the working of the model, and study the impact of each of the 21 features on the 4 class predictions (benign, defacement, phishing and malware).

With social media becoming the most frequently used mode of modern-day communications, the propagation of fake or vicious news through such modes of communication has emerged as a serious problem. The scope of the problem of fake or vicious news may range from rumour-mongering, with intent to defame someone, to manufacturing false opinions/trends impacting elections and stock exchanges to much more alarming and mala fide repercussions of inciting violence by bad actors, especially in sensitive law-and-order situations. Therefore, curbing fake or vicious news and identifying the source of such news to ensure strict accountability is the need of the hour. Researchers have been working in the area of using text analysis, labelling, artificial intelligence, and machine learning techniques for detecting fake news, but identifying the source or originator of such news for accountability is still a big challenge for which no concrete approach exists as of today. Also, there is another common problematic trend on social media whereby targeted vicious content goes viral to mobilize or instigate people with malicious intent to destabilize normalcy in society. In the proposed solution, we treat both problems of fake news and vicious news together. We propose a blockchain and keyed watermarking-based framework for social media/messaging platforms that will allow the integrity of the posted content as well as ensure accountability on the owner/user of the post. Intrinsic properties of blockchain-like transparency and immutability are advantageous for curbing fake or vicious news. After identification of fake or vicious news, its spread will be immediately curbed through backtracking as well as forward tracking. Also, observing transactions on the blockchain, the density and rate of forwarding of a particular original message going beyond a threshold can easily be checked, which could be identified as a possible malicious attempt to spread objectionable content. If the content is deemed dangerous or inappropriate, its spread will be curbed immediately. The use of the Raft consensus algorithm and bloXroute servers is proposed to enhance throughput and network scalability, respectively. Thus, the framework offers a proactive as well as reactive, practically feasible, and effective solution for curtailment of fake or vicious news on social media/messaging platforms. The proposed work is a framework for solving fake or vicious news spread problems on social media; the complete design specifications are beyond scope of the current work and will be addressed in the future.

The essence of “blockchain” is a shared database in which information stored is un-falsifiable, traceable, open, and transparent. Therefore, to improve the security of private information in medical systems, this article uses blockchain technology to design a method to protect private information in medical systems and effectively realize anti-theft control of private information. First, the Patient-oriented Privacy Preserving Access Control model is introduced into the access control process of private information in medical systems. Next, a private information storage platform is built by using blockchain technology, and information transmission is realized using standard cryptographic algorithms. In this process, file authorization contracts are also used to guarantee the security of private information and further prevent theft of medical private information. Our simulation results show that the storage response time of this method is kept below 1,000 ms, and the maximum information throughput rate reaches 550 kbit/s, which indicates that this method has strong performance in information storage and transmission efficiency. Moreover, the reliability and bandwidth utilization of data transmission across domains is higher, so the method has higher information security control performance and superior overall performance.

In the news industry, as well as in social media, fake news detection and identification of news sources has become a central topic of discussion. In the era of digitization, anyone can easily generate or manipulate digital content and publish them on social media websites. On the one hand, these social networking platforms provide ample ease in modern-day communication but on the other hand, using such platforms has posed new challenges to real-world implementation like viral spreading of false/fake information with malicious intentions. In this paper, a naive blockchain and watermarking based social media framework is proposed to control the fake news propagation. We postulate a new blockchain model to mitigate existing challenges in this field. Moreover, the novel solution can help in reducing the spread of fake news by tracing the root or origin of the fake news on social media. Through our experimental results, we show that our blockchain-based solution is able to immediately stream data through a bloXroute server that can propagate data up to 100 times faster than conventional solutions.

The unexpected development and quick; however, the uncontrolled overall spread of the Coronavirus shows us the disappointment of existing human services observation frameworks to convenient handle general wellbeing crises. In spite of the fact that upgrades in medicinal services observation have been understood, these still miss the mark in forestalling commotion. Absence of important advances taken to guarantee control and following of the infection have bothered the circumstance. Blockchain innovation has progressively been referenced as an instrument to help with different parts of various applications. This paper highlights the role of blockchain in forestalling the future of pandemics. Various use cases of blockchain technology that can help in the battle against the COVID-19 are also highlighted in this paper.

We investigate six authenticated encryption schemes (ACORN, ASCON-128a, ICEPOLE-128a, Ketje Jr, MORUS, and NORX-32) from the CAESAR competition. We aim at state recovery attacks using a SAT solver as a main tool. Our analysis reveals that these schemes, as submitted to CAESAR, provide strong resistance against SAT-based state recoveries. To shed a light on their security margins, we also analyse modified versions of these algorithms, including round-reduced variants and versions with higher security claims. Our attacks on such variants require only a few known plaintext-ciphertext pairs and small memory requirements (to run the SAT solver), whereas time complexity varies from very practical (few seconds on a desktop PC) to 'theoretical' attacks.

In this paper we investigate the security margin of MORUS-an authenticated cipher taking part in the CAESAR competition. We propose a new key recovery approach, which can be seen as an accelerated exhaustive search. We also verify the resistance of MORUS against internal differential and rotational cryptanalysis. Our analysis reveals that the cipher has a solid security margin and a lack of round constants does not bring any weakness. Our work helps to reliably evaluate this new, high-performance algorithm, which is particularly important in the context of the ongoing CAESAR competition.

In this work we focus on the tweakable block cipher Scream, We have analysed Scream with the techniques, which previously have not been applied to this algorithm, that is differential-linear and impossible differential cryptanalysis. This is work in progress towards a comprehensive evaluation of Scream. We think it is essential to analyse these new, promising algorithms with a possibly wide range of cryptanalytic tools and techniques. Our work helps to realize this goal.