Copy trading has become the dominant entry strategy in meme coin markets. However, due to the market's extremely illiquid and volatile nature, the strategy exposes an exploitable attack surface: adversaries deploy manipulative bots to front-run trades, conceal positions, and fabricate sentiment, systematically extracting value from naïve copiers at scale. Despite its prevalence, bot-driven manipulation remains largely unexplored, and no robust defensive framework exists. We propose a manipulation-resistant copy-trading system based on a multi-agent architecture powered by a multi-modal large language model (LLM) and chain-of-thought (CoT) reasoning. Our approach outperforms zero-shot and most statistic-driven baselines in prediction accuracy as well as all baselines in economic performance, achieving an average copier return of 3% per meme coin investment under realistic market frictions. Overall, our results demonstrate the effectiveness of agent-based defenses and predictability of trader profitability in adversarial meme coin markets, providing a practical foundation for robust copy trading.

Abstract The rapid proliferation of fake news in digital media has emerged as a major threat to information credibility and public trust. Although recent advances have explored multimodal learning for fake news detection, existing models often fail to effectively integrate heterogeneous data sources and remain vulnerable to adversarial manipulations. To address these challenges, we propose (Multimodal Adversarial Deep Semantic Learning), a robust multimodal fake news detection framework that unifies generative adversarial networks (GANs) with supervised contrastive learning. Specifically, employs a multi-layer joint attention mechanism to align and fuse textual and visual features, while adversarial training encourages the extraction of event-invariant representations, enhancing generalizability across unseen news events. Additionally, contrastive learning with adversarial perturbations further strengthens feature discrimination and robustness against attacks. Extensive experiments on benchmark Twitter and Weibo datasets demonstrate that achieves state-of-the-art accuracy (85.3%) and maintains stable performance with only a 1.1% drop under adversarial conditions, outperforming existing methods in both detection accuracy and resilience. These results underscore ’s effectiveness in advancing robust multimodal fake news detection and promoting digital information integrity.

Automated Market Maker (AMM)-based Decentralized Exchanges (DEXs) are crucial in Decentralized Finance (DeFi), but Ethereum implementations suffer from high transaction costs and price synchronization challenges. To address these limitations, we compare the XRP Ledger (XRPL)-AMM-Decentralized Exchange (DEX), a protocol-level implementation, against a Generic AMM-based DEX (G-AMM-DEX) on Ethereum, akin to Uniswap’s V2 AMM implementation, through agent-based simulations using real market data and multiple volatility scenarios generated via Geometric Brownian Motion (GBM). Results demonstrate that the XRPL-AMM-DEX achieves superior price synchronization, reduced slippage, and improved returns due to XRPL’s lower fees and shorter block times, with benefits amplifying during market volatility. The integrated Continuous Auction Mechanism (CAM) further mitigates impermanent loss by redistributing arbitrage value to Liquidity Providers (LPs). To the best of our knowledge, this study represents the first comparative analysis between protocol-level and smart contract AMM-based DEX implementations and the first agent-based simulation validating theoretical auction mechanisms for AMM-based DEXs.

Privacy remains a significant challenge in public blockchain ecosystems. Mainstream add-on privacy solutions, such as Stealth Address Protocols (SAPs) and Zero-Knowledge Proof (ZKP)-based mixers, have recently attracted considerable attention. However, existing SAPs offer only ephemeral anonymity for users' transaction data, and their implementation and evaluation within the highly concurrent Unspent Transaction Output (UTXO) model remain largely unexplored. ZKP-based mixers are limited to native coin transfers with fixed denominations and require additional security assumptions, employing out-of-band encrypted channels to transmit notes. To overcome these challenges, we unify the core principles underlying both SAPs and ZKP mixers and formally introduce StealthHub, a UTXObased SAP. Compared with the widely adopted dual-key-based Umbra protocol prevalent on Ethereum Virtual Machine (EVM)-compatible chains, StealthHub reduces computational overhead for the prepare and scan announcements stages by over 71% and 32%, respectively. Furthermore, by leveraging Merkle Mountain Range (MMR) commitments and off-chain batch aggregation, our StealthHub implementation lowers deposit and shielded transfer transaction costs to approximately 76% of those for a standard transfer, substantially improving practical usability.

System logs are crucial sources of information for engineers to analyze and resolve anomalies and faults in large-scale software systems. However, logs on a distributed system are often fragmented, making it challenging to achieve unified processing and comprehension. Traditional methods for log-based anomaly detection often employ machine learning algorithms with a focus on log event counts or log sequences. However, traditional methods fall short of fully leveraging the temporal and spatial structures inherent in distributed system logs, leading to issues of false positives and unstable performance in anomaly detection. In this paper, we propose a novel log anomaly detection method based on the construction of distributed system workflow graphs. This method extracts spatio-temporal information from distributed system logs and constructs event workflow graphs. These graphs accurately reflect the execution of the system and provide more comprehensive support for anomaly detection based on distributed system logs. The experimental results demonstrated that STGraph achieved F1 scores of 0.959,0.979, and 0.959 on HDFS, BGL, and OpenStack datasets respectively, outperforming LogRobust, PLELog, and NeuralLog by 1.2%-18.6% across precision/recall metrics. Notably, it attained 0.985 recall on BGL and maintained >0.935 F1 scores under 30% noise interference, 21.8% higher than LogRobust.

Blockchain sharding has emerged as a promising solution to the scalability challenges in traditional blockchain systems by partitioning the network into smaller, manageable subsets called shards. Despite its potential, existing sharding solutions face significant limitations in handling dynamic workloads, ensuring secure cross-shard transactions, and maintaining system integrity. To address these gaps, we propose DynaShard, a dynamic and secure cross-shard transaction processing mechanism designed to enhance blockchain sharding efficiency and security. DynaShard combines adaptive shard management, a hybrid consensus approach, plus an efficient state synchronization and dispute resolution protocol. Our performance evaluation, conducted using a robust experimental setup with real-world network conditions and transaction workloads, demonstrates DynaShard's superior throughput, reduced latency, and improved shard utilization compared to the fast transaction scheduling in blockchain sharding (FTSBS) method. Specifically, DynaShard achieves up to a 42.6% reduction in latency and a 78.77% improvement in shard utilization under high transaction volumes and varying cross-shard transaction ratios. These results highlight DynaShard's ability to outperform state-of-the-art sharding methods, ensuring scalable and resilient blockchain systems. We believe that DynaShard's innovative approach will significantly impact future developments in blockchain technology, paving the way for more efficient and secure distributed systems.

Pump-and-Dump (PD) schemes pose a significant threat to the stability and fairness of Decentralized Finance (DeFi) markets, often resulting in substantial financial losses for investors. The early and accurate detection of these schemes is crucial for preserving trust in the rapidly expanding cryptocurrency ecosystem. However, existing detection methods primarily rely on post-event analysis and heuristic-based approaches, which are often inadequate for real-time and precise identification of PD activities. In this paper, we present PUMPWATCHER, an innovative framework that employs Graph Neural Networks (GNNs) and contrastive learning to detect PD schemes by modeling transaction behaviors within temporal graphs. PUMPWATCHER integrates advanced transaction graph construction, temporal GNNs, and contrastive learning techniques to enhance node and edge representations, thereby improving the detection of intricate and covert PD operations. We validate PUMPWATCHER on a dataset from Uniswap, encompassing 924,508 transactions across 858 tokens within December 2022. The results show that PUMPWATCHER outperforms state-of-the-art models, achieving a superior balanced accuracy of 92.3%, while significantly minimizing false positives and negatives. These outcomes highlight its potential to set a new standard in real-time detection of market manipulation, paving the way for more secure and resilient DeFi ecosystems.

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional Decentralized finance (DeFi) governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce “Auto.gov”, a learning-based governance framework that employs a Deep Q-network (DQN) Reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto.gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto.gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric—protocol profitability. Overall, the comprehensive evaluations confirm that Auto.gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.

The evolving landscape of Decentralized Finance (DeFi) has raised critical security concerns, especially pertaining to Protocols for Loanable Funds (PLFs) and their dependency on price oracles, which are susceptible to manipulation. The emergence of flash loans has further amplified these risks, enabling increasingly complex oracle manipulation attacks that can lead to significant financial losses. Responding to this threat, we first dissect the attack mechanism by formalizing the standard operational and adversary models for PLFs. Based on our analysis, we propose SecPLF, a robust and practical solution designed to counteract oracle manipulation attacks efficiently. SecPLF operates by tracking a price state for each cryptoasset, including the recent price and the timestamp of its last update. By imposing price constraints on the price oracle usage, SecPLF ensures a PLF only engages a price oracle if the last recorded price falls within a defined threshold, thereby negating the profitability of potential attacks. Our evaluation based on historical market data confirms SecPLF's efficacy in providing high-confidence prevention against arbitrage attacks that arise due to minor price differences. SecPLF delivers proactive protection against oracle manipulation attacks, offering ease of implementation, oracle-agnostic property, and resource and cost efficiency.

Logs play a crucial role in recording valuable system runtime information, extensively utilized by service providers and users for effective service management. A typical approach in service management, based on log analysis, involves parsing the original log messages initially presented in an unstructured format. Subsequently, a data mining model is employed to extract critical system behavior information, aiding in service management. As the volume of logs rapidly increases, training models using current log resolution methods post-log collection becomes excessively time-consuming, leading to decreased accuracy. Manual analysis of extensive logs is both time-intensive and inefficient. This article introduces Aclog, an automated log parsing tool tailored for large-scale log analysis, storage, and management. Aclog operates by storing and managing logs in a structured and unified format, thereby offering a cohesive database for comprehensive log auditing of computing systems. Key components of Aclog encompass the log updater, log parser, log storage, and log querier. In this paper, we utilize a realworld, large-scale public log dataset to showcase the capabilities of Aclog. We evaluate the log files generated by ten popular systems.

Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregation-layer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and loanable funds (PLFs) protocol for loanable funds (PLF). We examine the yield farming mechanism by first studying the operations encoded in the yield farming smart contracts, and then performing stylized, parameterized simulations on various yield farming strategies. We conduct a thorough literature review on related work, and establish a framework for yield farming protocols that takes into account pool structure, accepted token types, and implemented strategies. Using our framework, we characterize major yield aggregators in the market including Yearn Finance, Beefy, and Badger DAO. Moreover, we discuss anecdotal attacks against yield aggregators and generalize a number of risks associated with yield farming.

Yield farming has been an immensely popular activity for cryptocurrency holders since the explosion of Decentralized Finance (DeFi) in the summer of 2020. In this Systematization of Knowledge (SoK), we study a general framework for yield farming strategies with empirical analysis. First, we summarize the fundamentals of yield farming by focusing on the protocols and tokens used by aggregators. We then examine the sources of yield and translate those into three example yield farming strategies, followed by the simulations of yield farming performance, based on these strategies. We further compare four major yield aggregators—Idle, Pickle, Harvest and Yearn—in the ecosystem, along with brief introductions of others. We systematize their strategies and revenue models, and conduct an empirical analysis with on-chain data from example vaults, to find a plausible connection between data anomalies and historical events. Finally, we discuss the benefits and risks of yield aggregators.

To achieve interoperability between unconnected ledgers, hash time lock contracts (HTLCs) are commonly used for cross-chain asset exchange. The solution tolerates transaction failure, and can “make the best out of worst” by allowing transacting agents to at least keep their original assets in case of an abort. Nonetheless, as an undesired outcome, reoccurring transaction failures prompt a critical and analytical examination of the protocol. In this study, we propose a game-theoretic framework to study the strategic behaviors of agents taking part in cross-chain atomic swaps implemented with HTLCs. We study the success rate of the transaction as a function of the exchange rate of the swap, the token price and its volatility, among other variables. We demonstrate that in an attempt to maximize one's own utility as asset price changes, either agent might withdraw from the swap. An extension of our model confirms that collateral deposits can improve the transaction success rate, motivating further research towards collateralization without a trusted third party. A second model variation suggests that a swap is more likely to succeed when agents dynamically adjust the exchange rate in response to price fluctuations.

Scalability has been a bottleneck for major blockchains such as Bitcoin and Ethereum. Despite the significantly improved scalability claimed by several high-profile blockchain projects, there has been little effort to understand how their transactional throughput is being used. In this paper, we examine recent network traffic of three major high-scalability blockchains---EOSIO, Tezos and XRP Ledger (XRPL)---over a period of seven months. Our analysis reveals that only a small fraction of the transactions are used for value transfer purposes. In particular, 96% of the transactions on EOSIO were triggered by the airdrop of a currently valueless token; on Tezos, 76% of throughput was used for maintaining consensus; and over 94% of transactions on XRPL carried no economic value. We also identify a persisting airdrop on EOSIO as a DoS attack and detect a two-month-long spam attack on XRPL. The paper explores the different designs of the three blockchains and sheds light on how they could shape user behavior.