IET Blockchain is a Gold Open Access journal that publishes high quality research papers focusing on the latest advances in blockchain and its applications. The journal reports fundamental research results, cutting-edge technologies, latest developments and emerging applications of the blockchain technology.
Latest content
-
Security and privacy issues in blockchain and its applications
- Author(s): Liangmin Wang ; Victor S. Sheng ; Boris Düdder ; Haiqin Wu ; Huijuan Zhu
- + Show details - Hide details
-
p.
169
–171
(3)
-
WASMOD: Detecting vulnerabilities in Wasm smart contracts
- Author(s): Jianfei Zhou and Ting Chen
- + Show details - Hide details
-
p.
172
–181
(10)
AbstractOver the past few years, blockchain platforms supporting WebAssembly (Wasm) smart contracts are gaining popularity. However, Wasm smart contracts are often compiled from memory‐unsafe languages (e.g. C and C++). And there is a lack of effective defense against integer overflow and stack overflow at the compiler and virtual machine (VM) layers, making Wasm smart contracts even more exploitable than native C and C++ programs. In this paper, the authors propose wasm overflow detector (WASMOD) to address the integer overflow and stack overflow vulnerabilities. The authors’ approach combines bytecode instrumentation, run‐time validation, and grey‐box fuzzing to detect these vulnerabilities. The authors applied their approach to the popular EOSIO blockchain and evaluated it on 4616 deployed Wasm smart contracts. The authors’ approach detected 13 real‐world vulnerable smart contracts.
-
Waterfall: Gozalandia. Distributed protocol with fast finality and proven safety and liveness
- Author(s): Sergii Grybniak ; Yevhen Leonchyk ; Igor Mazurok ; Oleksandr Nashyvan ; Ruslan Shanin
- + Show details - Hide details
-
p.
182
–193
(12)
AbstractA consensus protocol is a crucial mechanism of distributed networks by which nodes can coordinate their actions and the current state of data. This article describes a BlockDAG consensus algorithm based on the Proof of Stake approach. The protocol provides network participants with cross‐voting for the order of blocks, which, in the case of a fair vote, guarantees a quick consensus. Under conditions of dishonest behavior, cross‐voting ensures that violations will be quickly detected. In addition, the protocol assumes the existence of a Coordinating network containing information about the approved ordering, which qualitatively increases security and also serves to improve network synchronization.
A consensus protocol is a crucial mechanism of distributed networks by which nodes can coordinate their actions and the current state of data. This article describes a BlockDAG consensus algorithm based on the Proof of Stake approach. The protocol provides network participants with cross‐voting for the order of blocks, which, in the case of a fair vote, guarantees a quick consensus. Under conditions of dishonest behavior, cross‐voting ensures that violations will be quickly detected. In addition, the protocol assumes the existence of a Coordinating network containing information about the approved ordering, which qualitatively increases security and also serves to improve network synchronization.image
-
Phishing detection on Ethereum via transaction subgraphs embedding
- Author(s): Haifeng Lv and Yong Ding
- + Show details - Hide details
-
p.
194
–203
(10)
AbstractWith the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph‐based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models.
We designed an improved subgraph embedding algorithm called imgraph2vec with biases of transaction blockheight, transaction timestamp and transaction amount for detection of phishing addresses. Experiments show that our designed detection framework has good performance in real‐world Ethereum transaction record verificationimage
-
BlockDetective: A GCN‐based student–teacher framework for blockchain anomaly detection
- Author(s): Jinglin Li ; Yihang Zhang ; Chun Yang
- + Show details - Hide details
-
p.
204
–212
(9)
AbstractThe anonymous and tamper‐proof nature of the blockchain poses significant challenges in auditing and regulating the behaviour and data on the chain. Criminal activities and anomalies are frequently changing, and fraudsters are devising new ways to evade detection. Moreover, the high volume and complexity of transactions and asymmetric errors make data classification more challenging. Also, class imbalances and high labelling costs are hindering the development of effective algorithms. In response to these issues, the authors present BlockDetective, a novel framework based on GCN that utilizes student–teacher architecture to detect fraudulent cryptocurrency transactions that are related to money laundering. The authors’ method leverages pre‐training and fine‐tuning, allowing the pre‐trained model (teacher) to adapt better to the new data distribution and enhance the prediction performance while teaching a new, light‐weight model (student) that provides abstract and top‐level information. The authors’ experimental results show that BlockDetective outperforms state‐of‐the‐art research methods by achieving top‐notch performance in detecting fraudulent transactions on the blockchain. This framework can assist regulators and auditors in detecting and preventing fraudulent activities on the blockchain, thereby promoting a more secure and transparent financial system.
The authors present BlockDetective, a novel framework based on GCN that utilizes student–teacher architecture to detect fraudulent cryptocurrency transactions that are related to money laundering. The authors’ method leverages pre‐training and fine‐tuning, allowing the pre‐trained model (teacher) to adapt better to the new data distribution and enhance the prediction performance while teaching a new, light‐weight model (student) that provides abstract and top‐level information. image
Most downloaded
Most cited
-
SmartOil: Blockchain and smart contract‐based oil supply chain management
- Author(s): AKM Bahalul Haque ; Md. Rifat Hasan ; Md. Oahiduzzaman Mondol Zihad
-
Applying blockchain for primary financial market: A survey
- Author(s): Ji Liu ; Zheng Xu ; Ruiqiang Li ; Hang Zhao ; Hongbo Jiang ; Jinhui Yao ; Dong Yuan ; Shiping Chen
-
Blockchain‐based reliable image copyright protection
- Author(s): Xiangli Xiao ; Xiaotong He ; Yushu Zhang ; Xuewen Dong ; Lu‐Xing Yang ; Yong Xiang
-
Waterfall: Gozalandia. Distributed protocol with fast finality and proven safety and liveness
- Author(s): Sergii Grybniak ; Yevhen Leonchyk ; Igor Mazurok ; Oleksandr Nashyvan ; Ruslan Shanin
-
Empirically comparing the performance of blockchain's consensus algorithms
- Author(s): Ashar Ahmad ; Abdulrahman Alabduljabbar ; Muhammad Saad ; DaeHun Nyang ; Joongheon Kim ; David Mohaisen