Mozafar Bag-Mohammadi

Fatemeh Shoaei, Mohammad Pishdar, Mozafar Bag-Mohammadi et al.

Rug-pull attacks pose a systemic threat across the blockchain ecosystem, yet research into early detection is hindered by the lack of scientific-grade datasets. Existing resources often suffer from temporal data leakage, narrow modality, and ambiguous labeling, particularly outside DeFi contexts. To address these limitations, we present TM-RugPull, a rigorously curated, leakage-resistant dataset of 1,028 token projects spanning DeFi, meme coins, NFTs, and celebrity-themed tokens. RugPull enforces strict temporal hygiene by extracting all features on chain behavior, smart contract metadata, and OSINT signals strictly from the first half of each project's lifespan. Labels are grounded in forensic reports and longevity criteria, verified through multi-expert consensus. This dataset enables causally valid, multimodal analysis of rug-pull dynamics and establishes a new benchmark for reproducible fraud detection research.

Fatemeh Shoaei, Mohammad Pishdar, Mozafar Bag-Mohammadi et al.

Smart contract-based ecosystems enable decentralized applications without trusted intermediaries, but their immutability and permissionless design also facilitate large-scale fraud. One of the most prevalent attacks is the rug pull, where project operators abruptly withdraw liquidity after artificially inflating token value. Existing detection methods primarily rely on reactive on-chain signals and often suffer from temporal data leakage, limiting their real-world reliability. This paper proposes a leakage-aware framework for early rug-pull detection that integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals. We construct a hand-labeled dataset of 1,000 token projects, spanning DeFi and non-DeFi settings, with all features extracted strictly prior to any liquidity withdrawal to preserve causal validity. The dataset combines structural on-chain indicators with external attention signals derived from social media activity and search trends. Within this framework, TabPFN is employed as a core modeling component for learning from multimodal tabular data under strict temporal constraints. Experimental results show that the proposed framework achieves strong discriminative performance and improved probability calibration compared to classical baselines, while maintaining low false-negative rates. By framing rug-pull detection as a causal, multimodal forecasting problem, this work emphasizes the necessity of leakage-resilient evaluation and calibrated risk estimation for deployment in blockchain security systems.

Mohammad-Javad Rezaei, Mozafar Bag-Mohammadi

In this paper, a new metaheuristic optimization algorithm, called Path Construction Imitation Algorithm (PCIA), is proposed. PCIA is inspired by how humans construct new paths and use them. Typically, humans prefer popular transportation routes. In the event of a path closure, a new route is built by mixing the existing paths intelligently. Also, humans select different pathways on a random basis to reach unknown destinations. PCIA generates a random population to find the best route toward the destination, similar to swarm-based algorithms. Each particle represents a path toward the destination. PCIA has been tested with 53 mathematical optimization problems and 13 constrained optimization problems. The results showed that the PCIA is highly competitive compared to both popular and the latest metaheuristic algorithms.