Due to the proliferation of ICT during the last few decades, there is an exponential increase in the usage of various smart applications such as smart farming, smart healthcare, supply-chain & logistics, business, tourism and hospitality, energy management etc. However, for all the aforementioned applications, security and privacy are major concerns keeping in view of the usage of the open channel, i.e., Internet for data transfer. Although many security solutions and standards have been proposed over the years to enhance the security levels of aforementioned smart applications, but the existing solutions are either based upon the centralized architecture (having single point of failure) or having high computation and communication costs. Moreover, most of the existing security solutions have focussed only on few aspects and fail to address scalability, robustness, data storage, network latency, auditability, immutability, and traceability. To handle the aforementioned issues, blockchain technology can be one of the solutions. Motivated from these facts, in this paper, we present a systematic review of various blockchain-based solutions and their applicability in various Industry 4.0-based applications. Our contributions in this paper are in four fold. Firstly, we explored the current state-of-the-art solutions in the blockchain technology for the smart applications. Then, we illustrated the reference architecture used for the blockchain applicability in various Industry 4.0 applications. Then, merits and demerits of the traditional security solutions are also discussed in comparison to their countermeasures. Finally, we provided a comparison of existing blockchain-based security solutions using various parameters to provide deep insights to the readers about its applicability in various applications.

Blockchain for Industry 4.0: A Comprehensive Review

In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.

/pdf/machine-learning-adoption-in-blockchain-based-smart-4oe5d1a6s0.pdf

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Blockchain envisioned UAV networks: Challenges, solutions, and comparisons

Electronic Health Records (EHRs) allows patients to control, share, and manage their health records among family members, friends, and healthcare service providers using an open channel, i.e., Internet. Thus, privacy, confidentiality, and data consistency are major challenges in such an environment. Although, cloud-based EHRs addresses the aforementioned discussions, but these are prone to various malicious attacks, trust management, and non-repudiation among servers. Hence, blockchain-based EHR systems are most popular to create the trust, security, and privacy among healthcare users. Motivated from the aforementioned discussions, we proposes a framework called as Blockchain-Based Deep Learning as-a-Service (BinDaaS). It integrates blockchain and deep-learning techniques for sharing the EHR records among multiple healthcare users and operates in two phases. In the first phase, an authentication and signature scheme is proposed based on lattices-based cryptography to resist collusion attacks among N-1 healthcare authorities from N. In the second phase, Deep Learning as-a-Service (DaaS) is used on stored EHR datasets to predict future diseases based on current indicators and features of patient. The obtained results are compared using various parameters such as accuracy, end-to-end latency, mining time, and computation and communication costs in comparison to the existing state-of-the-art proposals. From the results obtained, it is inferred that BinDaaS outperforms the other existing proposals with respect to the aforementioned parameters.

BinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications

Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.

/pdf/smart-contract-privacy-protection-using-ai-in-cyber-physical-1i4efbw1la.pdf

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges

Telesurgery has a huge potential to deliver a real-time healthcare surgical services to the remote or distant locations with high quality and accuracy over the wireless communication channel. It provides benefits to the society with improved precision and accuracy in diagnosis procedures. However, the existing telesurgery system has the security, privacy, and interoperability issues, which limits its applicability in healthcare centers across the world in future. To mitigate these issues, in this paper, we propose, a framework named as HaBiTs (blockchain-based secure and flawless inter-operable telesurgery), where security can be achieved with immutability and interoperability by Smart Contracts (SCs). SC is a piece of code written in solidity or other blockchain specific languages to establish the trust between all the parties connected through blockchain and also eliminate the need of an intermediary for data sharing. Finally, we highlight some issues of the traditional telesurgery system and how they are mitigated with usage of the proposed HaBiTs framework.

HaBiTs: Blockchain-based Telesurgery Framework for Healthcare 4.0

In the era of Industry 4.0, e-tourism uses bulk of digital payments through applications supported by heterogeneous payment gateways. These heterogeneous payment gateways open the doors for the attackers to perform malicious activities such as-hacking of wallet accounts, identity theft, attacks on payment clearance cycles. In e-tourism, financial data is maintained in a centralized cloud server, which can lead to payment failures during peak traffic. The aforementioned issues can be addressed by the usage of a decentralized mechanism such as-blockchain, which enables trust and reputation management among various stakeholders such as-banks, travel agencies, airports, railways, cruises, hotels, restaurants, and local taxis. Motivated by the above discussion, we propose a framework named as BloHosT (Blockchain Enabled Smart Tourism and Hospitality Management), which allows tourists to interact with various stakeholders through a single wallet identifier linked with a cryptocurrency server to initiate payments. BloHosT uses an immutable ledger, where no proofs are required during travel that provides a hassle-free experience to tourists. Also, a Tourism enabled Deep-Learning (TeDL) framework is presented as a part of BloHosT framework, which is trained on experience of previous visited travelers. It provides rating scores to prospective travelers about the recently visited locations by previous travelers. Finally, through case studies, we demonstrate that BloHosT achieves a high Return of Investment (ROI) in tourism sector as compared to traditional frameworks.

BloHosT: Blockchain Enabled Smart Tourism and Hospitality Management

Currently, almost any kind of data is stored on the Internet, which makes security issues of the private data very crucial. The main objective of this paper is to analyze multimodal authentication security systems that are combined with keystroke dynamics (KSD). The keystroke dynamics are one of the behavioral biometrics which describes the typing rhythm of the individual. The proposed work aimed to design a multimodal authentication system using a machine learning/deep learning algorithm for user classification based on traditional and touchscreen features of keystroke dynamics. The results have shown that XGBoost has a higher overall classification accuracy (90.91%) in comparison with the previous works (85.90%) on the same dataset. Confusion Matrix and Receiver Operating Characteristics (ROC) curves were derived for further performance evaluation of the system. Area Under the Curve (AUC) values for each of the classes and for the whole classifier were also reported.

Analysis of Authentication System Based on Keystroke Dynamics

A deniable authentication (DA) protocol plays a vital role to provide security and privacy of the mobile nodes in a mobile ad hoc network (MANET). In recent years, a number of similar works have been proposed, but most of them experience heavy computational and communication overhead. Further, most of these protocols are not secure against different attacks. To address these concerns, we devised an identity-based deniable authentication (IBDA) protocol with adequate security and efficiency. The proposed IBDA protocol is mainly designed for MANETs, where the mobile devices are resource-limited. The proposed IBDA protocol used the elliptic curve cryptography (ECC) and identity-based cryptosystem (IBC). The security of our IBDA protocol depends on the elliptic curve discrete logarithm (ECDL) problem and bilinear Diffie-Hellman (BDH) problem.

A Secure Identity-based Deniable Authentication Protocol for MANETs

Using private cars by parents to pick up children from school is one of the causes of traffic congestion. The existing school district division method is still not reasonable to solve the problem of student enrollment in nearby schools to their residence. With the support of the data provided by Baidu Map API and Beijing Municipal Education Commission, this paper proposes a multi-target school district division method. This method is realized by using combinatorial optimization with the three constraints–school capacity, road length and time consumption. The k-nearest neighbor algorithm was used to preprocess the student data, and the compound genetic algorithm was used to find the optimal combination of school district re-planning. We used the data of Shijingshan District of Beijing for simulation and visualized the traffic network status before and after school district re-planning. The experimental results confirmed that this method can effectively reduce the time of driving and alleviate the road congestion during morning and evening rush hours, which has reference significance for the existing school district planning scheme.

Mohammad S. Obaidat

Papers

HaBiTs: Blockchain-based Telesurgery Framework for Healthcare 4.0

BloHosT: Blockchain Enabled Smart Tourism and Hospitality Management

Analysis of Authentication System Based on Keystroke Dynamics

A Secure Identity-based Deniable Authentication Protocol for MANETs

The School District Reorganization by Combining with Traffic Congestion Data