| Title | Parasite Chain Attack Detection in the IOTA Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Ghaffaripour, Shadan, Miri, Ali |
| Conference Name | 2022 International Wireless Communications and Mobile Computing (IWCMC) |
| Keywords | anomaly detection, blockchain, blockchains, Chained Attacks, directed acyclic graph, distributed ledger, Double-spend Attack, dynamic graph, Image edge detection, Internet of Things, IOTA, mobile computing, Parasite Chain, pubcrawl, resilience, Resiliency, Scalability, Tangle, Wireless communication |
| Abstract | Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network. |
| Notes | ISSN: 2376-6506 |
| DOI | 10.1109/IWCMC55113.2022.9824318 |
| Citation Key | ghaffaripour_parasite_2022 |
Parasite Chain Attack Detection in the IOTA Network