The Internet of Things (IoT) is considered a high-impact technology in many major markets. It promises many opportunities, but there are several risks and drawbacks associated with this technology stand in the way of realizing its potential opportunities, such as the IoT devices limited capabilities (restricted resources) as well as the current access control systems based on central structures and central hierarchies. with all IoT devices, the security risks and threats are enormous. Blockchain is defined as tamper-proof, tamper-resistant digital ledgers that are executed in a distributed fashion, i.e., without a central repository and usually without central authority, allowing all parties to track information over an insecure network without the need for third-party verification. This technology could be a solution to the problems facing the Internet of Things, however the integrating between blockchain and Internet of Things system comes with many challenges as such blockchain suffers from high resource consumption, slow response, etc. In this research we will propose a new architecture for how to integrate blockchain with the Internet of Things. This structure consists of the smart home responsible for its own data (own chain) and the service provider which considered indirect manager of the network. The performance of the proposed architecture will be studied in terms of expansion, decentralization, security and permanent availability.
The sweeping scale embracing of the smart things has a profound effect in bringing about big data explosion and sophisticated cyber threats at the network edge. Edge computing needs novel security architectures to address the security challenges in severely resource-constrained and heterogeneous edge systems. The successful design of such architectures solves the challenges of scalability [handling massive Internet of Things (IoT) devices], resource constraints and high latency for critical applications. The existing security architectures such as the cloud fail to satisfy these requirements due to remoteness and centralization. This inculcates the need for a distributed architecture that offloads overheads from the cloud and IoT devices by deploying security parameters and operations in proximity. Specifically, the proliferation of distributed architecture integrated with intelligent algorithms such as artificial intelligence (AI) has paramount importance in providing a security collaboration, autonomy and efficiency for the IoT with an improved level of robustness and resilience. However, the state-of-the-art distributed architectures are victims of integrity cyber-attacks such as insider and adversarial attacks. To solve these problems, inspired by the applications in financial industries, the emerging distributed ledger-based technology architecture can significantly boost the robustness of edge network if it is adopted for edge security architecture that can leverage AI algorithms. In this chapter, the architectures of edge computing, specifically, blockchain-based edge architecture which is fueled with the power of AI algorithms, have been proposed and analyzed.
To solve the problem that the safety data in the process of coal mine production are easy to be maliciously tampered with and deleted, a mine consortium blockchain data security monitoring system is proposed. The coal mine consortium blockchain includes supervision department, builds favourable centralized and decentralized production mode, and improves PBFT (Practical Byzantine Fault Tolerance) consensus mechanism to implement practical coal mine safety production. The evaluation shows that the architecture we proposed is more appropriate and efficient for the mine Internet of Things than the traditional blockchain architecture. The Hyperledger Fabric platform is used to build the mine consortium blockchain system to achieve the sensor data reliability, node consensus, safe operation automation management, and major equipment traceability.
Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines.
The increasing prevalence and sophistication of autonomous vehicles are intricately tied with the advancements in Artificial Intelligence (AI), particularly in refining their network architectures. This technical exposition unfolds the paramount role of AI in autonomous vehicles, spotlighting its capabilities in sensor data processing, decision - making, and fostering Vehicle - to - Everything (V2X) communications. Moreover, the article elucidates the evolution and enhancement of network architectures through AI - driven technologies such as edge computing, centralized data centers, Distributed Ledger Technologies (DLT), and 5G communications. An additional layer of exploration is provided in network security, with AI safeguarding vehicular networks through anomaly detection and ensuring secure data transmission. This interplay between AI and network architecture elevates autonomous vehicles' operational efficiency and safety and acts as a linchpin in realizing a coherent and intelligent transportation ecosystem. The comprehensive integration of AI within the network forms the foundation for autonomous vehicles to navigate within an interconnected mobility infrastructure harmoniously and securely.
The consensus algorithm is the core component of a blockchain system, which determines the efficiency, security, and scalability of the blockchain network. The representative consensus algorithm is the proof of work (PoW) proposed in Bitcoin, where the consensus process consumes large amount of compute in solving meaningless Hash puzzel. Meanwhile, the deep learning (DL) has brought unprecedented performance gains at heavy computate cost. In this demo, we channels the otherwise wasted computational power to the practical purpose of training neural network models, through the proposed proof of learning (PoL) consensus algorithm. In PoLe, the training/testing data are released to the entire blockchain network (BCN) and the consensus nodes train NN models on the data, which serves as the proof of learning. When the consensus on the BCN considers a NN model to be valid, a new block is appended to the blockchain. Through our system, we investigate the potential of enpowering machine learning with consensus building on blockchains.
Abstract Blockchain originated in 2009, it was the underlying “ledger” recording technology of Bitcoin. After several years of development and improvement, it has gradually become a distributed, decentralized, and trustless technical solution. In the past two years, blockchain has become a new decentralized distributed computing paradigm. We apply blockchain technology into cloud computing, using the former’s security mechanism to improve the latter’s secure storage and secure computing performance. This is a promising research topic. The paper mainly discusses the design and implementation of blockchain from a technical prospect, it summarizes the advantages and disadvantages of the blockchain, and it also analyzes the possible application scenarios.
Blockchain and Artificial intelligence are novel technologies which take prominent place across all industries. AI refers to intelligent tasks that are carried out by machines which in-turn are done by humans in olden days. Blockchain a collection of decentralised networks which revolutionizes and upgrades the business operations. Blockchain creates a decentralised environment that shares data which is in encrypted form between ledgers in a confidential way without the involvement of any third party. Blockchain and Artificial Intelligence has been shaping their paths with a slight overlap of their own. Combined decentralised AI networks enables the businesses to take decisions without the need of centralised control activity. Block chain consists of nodes and it follows distributed ledger technology; Proof of Work (PoW) consensus algorithm used in blockchain makes use of lot of computing power and energy for the miners to get reward. To overcome this difficulty, this paper proposes AI enabled Miner Node Selection Algorithm in Block chain Networks based on PoW consensus algorithm. The paper presents a miner node selection with PoW in blockchain which reduce data storage in blockchain by data pruning technique. By executing this algorithm in blockchain network, unbiased blockchain implementation will be easier. The integration of AI with the implementation of blockchain enhances the efficiency of blockchain network by reducing the computational power, energy and time spent in selection of the node which is evident in our experimental results.
Shakila Zaman, Muhammad R. A. Khandaker, Risala Tasin Khan, Faisal Tariq · 5 authors
The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.
Technology grows up, day to day to facilitate human beings’ lifestyle, in order to protect them and their properties. Cloud computing is a platform that one hardware is shared by different clients in the virtual ways and works as standalone physical hardware, is accessed everywhere at any time through the internet. Security is an essential and a vital point to all customers is belonged in the same physical component. This survey paper discusses about cloud computing challenges, types of attacks, and currently solutions in the details. Furthermore, cloud security of models as application, network, and deployment and also services. Cloud security based on a distributed ledger helps the clients to operate within transparency manners and is decentralized to enhance cloud computing information security principals, cloud security requirements, cloud security control, and security design of cloud computing
Gunasekaran Manogaran, Mamoun Alazab, P. Mohamed Shakeel, Ching‐Hsien Hsu
Industrial Internet of Things is focused to improve the performance of smart factories through automation and scalable functions. IoT paradigm, information and communication technology, and intelligent computing are assimilated as a single entity for industrial automation, optimization, sharing and security, and scalability. In a view of the security requirement in smart industry data sharing through IoT, this article introduces a blockchain-assisted secure data sharing (BSDS) model. This model is responsible for administering inbound and outbound security in data acquisition and dissemination. The inbound acquisition is first classified using recurrent learning to identify adverse sequences in data dissemination. In the outbound security measure, end-to-end authentication based on the blockchain information of reputation and sequence differentiation is engaged. The blockchain paradigm controls the data gathering and dissemination instances through the classification and integrity verification in both the industry and processing terminals. For this purpose, the functions of the blockchain are riven for data gathering and monitoring in the smart industry whereas integrity and sequence verification is performed by the nonmining blockchain terminal in the processing environment. The integrated security measures are capable of maximizing the response rate by confining false alarm progression, failure rate, and time delay. Statistical analysis shows that the BSDS achieves a 5.67% high response rate and reduces the failure rate by 2.14%. Further, it achieves 3.12%, maximizes response rate by 6.63%, and reduces delay by 11.91%, respectively.
Rachit Agarwal, Tanmay Thapliyal, Sandeep K. Shukla
Different types of malicious activities have been flagged in multiple\npermissionless blockchains such as bitcoin, Ethereum etc. While some malicious\nactivities exploit vulnerabilities in the infrastructure of the blockchain,\nsome target its users through social engineering techniques. To address these\nproblems, we aim at automatically flagging blockchain accounts that originate\nsuch malicious exploitation of accounts of other participants. To that end, we\nidentify a robust supervised machine learning (ML) algorithm that is resistant\nto any bias induced by an over representation of certain malicious activity in\nthe available dataset, as well as is robust against adversarial attacks. We\nfind that most of the malicious activities reported thus far, for example, in\nEthereum blockchain ecosystem, behaves statistically similar. Further, the\npreviously used ML algorithms for identifying malicious accounts show bias\ntowards a particular malicious activity which is over-represented. In the\nsequel, we identify that Neural Networks (NN) holds up the best in the face of\nsuch bias inducing dataset at the same time being robust against certain\nadversarial attacks.\n
Cryptography algorithms play a vital role in Information Security and Management. To test the credibility, reliability of metadata exchanged between the sender and the recipient party of IoT applications different algorithms must be used. The hashing is also used for Electronic Signatures and based on how hard it is to hack them; various algorithms have different safety protocols. SHA-1, SHA-2, SHA3, MD4, and MD5, etc. are still the most accepted hash protocols. This article suggests the relevance of hash functions and the comparative study of different cryptographic techniques using blockchain technology. Cloud storage is amongst the most daunting issues, guaranteeing the confidentiality of encrypted data on virtual computers. Several protection challenges exist in the cloud, including encryption, integrity, and secrecy. Different encryption strategies are seeking to solve these problems of data protection to an immense degree. This article will focus on the comparative analysis of the SHA family and MD5 based on the speed of operation, its security concerns, and the need of using the Secure Hash Algorithm.
Abdullah Lakhan, Mazhar Ali Dootio, Ali Hassan Sodhro, Sandeep Pirbhulal · 7 authors
These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.
Muhammad Attique Khan, Inzamam Mashood Nasir, Muhammad Sharif, Majed Alhaisoni · 7 authors
Wireless Capsule Endoscopy (WCE) is an imaging technology, widely used in medical imaging for stomach infection recognition. However, a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured. The privacy of patients is very important and manual inspection is time consuming and costly. Therefore, an automated system for recognition of stomach infections from WCE frames is always needed. An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding. Initially, images are normalized in fixed dimension and passed in pre-trained deep models. These architectures are modified at each layer, to make them safer and more secure. Each layer contains an extra block, which stores certain information to avoid possible tempering, modification attacks and layer deletions. Information is stored in multiple blocks, i.e., block attached to each layer, a ledger block attached with the network, and a cloud ledger block stored in the cloud storage. After that, features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function. The Softmax classifier is applied at the end for final classification. Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%. The statistical analysis and individual model comparison show the proposed method’s authenticity.
In recent years, Blockchain technology has been mentioned for many times in the fields of finance, Internet and energy, and it is a distributed data ledger that is decentralized, authentic, reliable, traceable open and transparent. Blockchain is constantly figuring out how to integrate with other industries. This paper analyzes the current situation in the field of medical devices of China by combing the Annual Report on Medical Device Adverse Event Monitoring from 2013-2019, and using the compound annual average growth rate. The analysis found that this field has pain points such as lack of third-party regulation and difficulty in traceability, and then it proposed the specific application of blockchain technology in the field of medical devices to solve the problems in this field.
Ming Li, Leilei Zeng, Le Zhao, Renlin Yang · 6 authors
With the application of multimedia big data, the problems such as information leakage and data tampering have emerged. The security of images which is one of the most typical multimedia has become a major problem facing the large-scale open network environment. This paper proposed a blockchain-watermarking scheme to protect the privacy, integrity and availability of compressed sensed images, which effectively combines multimedia watermarking, compressed sensing, Interplanetary File System (IPFS) and blockchain technologies. Based on the reliable authentication of watermarking, the confidentiality protection of compressed sensing, the secure storage of IPFS, and the decentralization and non-tamperability of blockchain, the all-round security protection of the image big data based on compressive sensing can be realized. Experiments show that the proposed scheme is effective and feasible.
Open access
Advanced Steganography and Watermarking Techniques