Blockchain plays a vital task in cybersecurity. With the exerted efforts for realising large-scale quantum computers, most current cryptographic mechanisms may be hacked. Accordingly, we need a quantum tool utilised for designing blockchain frameworks to have the ability to be executed in the level of digital computers and resist the probable attacks from both digital and quantum computers. Quantum walks may be utilised as a quantum-inspired model for designing new cryptographic algorithms. In this paper, we present a new authentication and encryption protocol based on quantum-inspired quantum walks (QIQW). The proposed protocol is utilized to build a blockchain framework for secure data transmission among IoT devices. Instead of using classical cryptographic hash functions, quantum hash functions based on QIQW are employed for linking blocks of the chain. The main advantages of the presented framework are helping IoT nodes to effectively share their data with other nodes and full control of their records. Security analysis demonstrates that our proposed protocol can defend against message attack and impersonation attacks, thus ensuring secure transmission of data among IoT devices.

Quantum-Inspired Blockchain-Based Cybersecurity: Securing Smart Edge Utilities in IoT-Based Smart Cities

In the context of blockchain technology, “off-chain” refers to computation or data that is structurally external to the blockchain network. Off-Chain Blockchain Systems (OCBS) enable this information processing and management through distributed software architecture where the blockchain network interacts with off-chain resources. Hence, OCBS are a critical data governance component in the design of enterprise blockchain solutions, resulting in extensive research and development exploring the interplay between on-chain and off-chain storage and computation and efforts to evaluate their performance relative to other information management systems. Key features of OCBS’ are their ability to improve scalability, reduce data storage requirements, and enhance data privacy, all extremely critical issues to enable broader blockchain adoption. These OCBS features map well to the needs of the healthcare industry, particularly due to the need to manage various types of medical, consumer, and other health-related data. However, different types of health data are also subject to stringent regulatory, security and legal requirements, a key factor limiting blockchain adoption in the sector. In response, there is a critical need to better align OCBS design features to different types of healthcare data management and their respective governance and privacy regimes. This article first reviews the characteristics of different constructs of OCBS. It then proposes a modular hybrid privacy-preserving framework leveraging off-chain and on-chain blockchain system design applied to three different reference models that illustrate how blockchain can enhance healthcare information management. Through this privacy-preserving framework we hope to liberate healthcare data by enabling sharing, sovereignty and enhanced trust.

hOCBS: A privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design

Blockchain is a revolutionary technology that enables users to communicate in a trust-less manner. It revolutionizes the modes of business between organizations without the need for a trusted third party. It is a distributed ledger technology based on a decentralized peer-to-peer (P2P) network. It enables users to store data globally on thousands of computers in an immutable format and empowers users to deploy small pieces of programs known as smart contracts. The blockchain-based smart contract enables auto enforcement of the agreed terms between two untrusted parties. There are several security vulnerabilities in Ethereum blockchain-based smart contracts, due to which sometimes it does not behave as intended. Because a smart contract can hold millions of dollars as cryptocurrency, so these security vulnerabilities can lead to disastrous losses. In this paper, a systematic review of the security vulnerabilities in the Ethereum blockchain is presented. The main objective is to discuss Ethereum smart contract security vulnerabilities, detection tools, real life attacks and preventive mechanisms. Comparisons are drawn among the Ethereum smart contract analysis tools by considering various features. From the extensive depth review, various issues associated with the Ethereum blockchain-based smart contract are highlighted. Finally, various future directions are also discussed in the field of the Ethereum blockchain-based smart contract that can help the researchers to set the directions for future research in this domain.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09667515.pdf

Systematic Review of Security Vulnerabilities in Ethereum Blockchain Smart Contract

The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks. The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single security solution. However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks. We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them. We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. We also perform 10-fold cross-validation, proving our results to be unbiased, and compare our results with current benchmark algorithms.

SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT

The sharing of electronic medical records (EMRs) has great positive significance for research on disease and epidemic prevention. Recently, blockchain-based eHealth systems have achieved great success in terms of EMRs sharing and management, but there still remain some challenges. Permissioned blockchain-based solutions provide high throughput and scalability, but may suffer from rollback attacks and lead to privacy leakage. Designs based on the public blockchain, on the other hand, are more open and secure, but sacrifice scalability and have no incentives for medical institutions to join into the systems. Moreover, data retrieval in blockchain-based eHealth systems is inefficient because of the basic blockchain structure. To solve the above problems, we propose a blockchain-based medical data sharing and privacy-preserving eHealth system named SPChain. To achieve quick retrieval, we devise special keyblocks and microblocks for patients to store their EMRs. A reputation system is also constructed to motivate medical institutions to participate in SPChain. By using proxy re-encryption schemes, SPChain achieves medical data sharing for patients in a privacy-preserving manner. To evaluate SPChain, we leverage the distribution of miners in the real world to test the system’s performance and ability to resist mentioned attacks. The results show that SPChain can achieve high throughput (220 TPS) with low storage overhead. Compared with the existing schemes, SPChain achieves lower time complexity in terms of data retrieving, and can resist proposed blockchain attacks as well as SPChain attacks.

SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system

Blockchain technology brings innovation to various industries. Ethereum is currently the second blockchain platform by market capitalization, it’s also the largest smart contract blockchain platform. Smart contracts can simplify and accelerate the development of various applications, but they also bring some problems. For example, smart contracts are used to commit fraud, vulnerability contracts are deliberately developed to undermine fairness, and there are numerous duplicative contracts that waste performance with no actual purpose. In this paper, we propose a transaction-based classification and detection approach for Ethereum smart contract to address these issues. We collected over 10,000 smart contracts from Ethereum and focused on the data behavior generated by smart contracts and users. We identified four behavior patterns from the transactions by manual analysis, which can be used to distinguish the difference between different types of contracts. Then 14 basic features of a smart contract are constructed from these. To construct the experimental dataset, we propose a data slicing algorithm for slicing the collected smart contracts. After that, we use an LSTM network to train and test our datasets. The extensive experimental results show that our approach can distinguish different types of contracts and can be applied to anomaly detection and malicious contract identification with satisfactory precision, recall, and f1-score.

Transaction-based classification and detection approach for Ethereum smart contract

In the current intranet environment, information is becoming more readily accessed and replicated across a wide range of interconnected systems. Anyone using the intranet computer may access content that he does not have permission to access. For an insider attacker, it is relatively easy to steal a colleague’s password or use an unattended computer to launch an attack. A common one-time user authentication method may not work in this situation. In this paper, we propose a user authentication method based on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity authentication on current computer users, thus to address insider threats. We used an open-source dataset with ten users to carry out experiments, and the experimental results demonstrated the effectiveness of the approach. This approach can complete a user authentication task approximately every 7 seconds, with a false acceptance rate of 2.94% and a false rejection rate of 2.28%.

/pdf/an-insider-threat-detection-approach-based-on-mouse-dynamics-38lsszjkbe.pdf

An Insider Threat Detection Approach Based on Mouse Dynamics and Deep Learning

A Novel Smart Contract Reentrancy Vulnerability Detection Model based on BiGAS

Blockchain, as a decentralized database, provides natural anonymity and tamper-resistance, safeguarding the identity of both parties in covert communication and preventing malicious third parties from tampering with secret messages. Compared to traditional covert communication over a network, blockchain offers a more reliable communication carrier. However, the strict order between special transactions in blockchain covert communication increases the time consumption of communication. This paper proposes a label-tree-based covert communication model, named LTCC, which uses dynamic labels with tree-like relationships to maintain the connections between special transactions. LTCC allows senders to upload transactions containing secret messages in a single pass, free from the high latency of blockchains. Even in the event of a failed transaction submission, it is possible to regenerate the transaction containing a specific label to be submitted to the blockchain, avoiding the reuse of addresses. The experimental results indicate that LTCC possesses excellent qualities such as concealment, reliability, robustness, and communication efficiency. Therefore, it can be feasibly implemented in the present-day, mainstream blockchain systems. 

A covert channel over blockchain based on label tree without long waiting times

We propose a novel smart contract re-entry vulnerability detection model based on BiGAS. The model combines a BiGRU neural network that introduces an attention mechanism with an SVM. We start from the data features of smart contracts, learn the model layer by layer to achieve feature extraction and vulnerability identification, introduce batch normalization, Dropout processing and use improved model classifiers to improve the vulnerability identification accuracy, model convergence speed and generalization capability of smart contracts. We had conducted numerous experiments, and the experimental results showed that BiGAS Detection Model has a strong vulnerability detection ability. The accuracy of vulnerability detection reached 93.24%, and the F1-score was 93.17%. We compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities. 

Yuan Liu

Papers

Transaction-based classification and detection approach for Ethereum smart contract

An Insider Threat Detection Approach Based on Mouse Dynamics and Deep Learning

A Novel Smart Contract Reentrancy Vulnerability Detection Model based on BiGAS

A covert channel over blockchain based on label tree without long waiting times

An Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities