Abstract In recent years, the widespread adoption of Ethereum-based transactions, such as cryptocurrencies and blockchain technologies, have revolutionized the way financial transactions are conducted. These decentralized and transparent systems offer numerous advantages, including enhanced security, immutability, and reduced transaction costs. However, alongside their benefits, Ethereum-based transactions have also attracted the attention of malicious actors seeking to exploit unsuspecting users through phishing scams. Phishing scams have thus become frequent in this scenario. Therefore, it is required to implement an effective and reliable phishing scam detection method. In this paper, we present the implementation of a highly efficient detection method by carrying out a graph-like data network formation, over which we then apply models that are based on graph neural networks like Magnet Link Prediction and Graph AutoEncoder Pathfinder Discovery Network Algorithm (GAE_PDNA). This helps in extracting useful information from the nodes of the graph. After relevant embeddings have been obtained, the classification of the phishing account is performed using AdaBoost classifier that helps in complex decision-making and detects the accounts related to the phishing scams. Our best model attains a precision of 0.99 and an F1 score of 0.99. Highlights

We investigate the problem of runtime analysis of concurrent C11 programs under Multi-Copy-Atomic semantics (MCA). Under MCA, one can analyze program outcomes solely through interleaving and reordering of thread events. As a result, obtaining intuitive explanations of program outcomes becomes straightforward. Newer versions of ARM (ARMv8 and later), Alpha, and Intel’s x-86 support MCA. Our tests reveal that state-of-the-art dynamic verification techniques that analyze program executions under the C11 memory model report safety property violations that can be interpreted as false alarms under MCA semantics. Sorting the true from false violations puts an undesirable burden on the user.In this work, we provide a dynamic verification technique (MoCA) to analyze C11 program executions which are permitted under the MCA model. We restrict C11 happens-before relation and propose coherence rules to capture precisely those C11 program executions which are allowed under the MCA model. MoCA’s exploration of the state-space is based on the state-of-the-art dynamic verification algorithm, source-DPOR. Our experiments validate that MoCA captures all coherent C11 program executions, and is precise for the MCA model.