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Alberto Maria Mongardini

Publications and source records attributed to Alberto Maria Mongardini.

7 recordsLinked to original sources

Is That Really My X-Ray? Measuring Internet-Exposed DICOM Services in the Presence of Deception

DICOM is the dominant protocol for exchanging medical images, yet many Internet-facing deployments lack basic security controls, exposing sensitive patient data to unauthorized access. Accurately measuring this exposure is complicated by honeypots, network telescopes, and other measurement artifacts that inflate published estimates. This paper presents a noise-aware study of Internet-facing DICOM services, combining active IPv4-wide scanning with passive honeypot deployments. We scan common DICOM ports using an ethics-constrained probe limited to association negotiation and C-ECHO. Then, we introduce a reproducible false-positive filtering method that identifies honeypots, telescopes, malformed responders, and other deception artifacts, reducing apparent exposure by 39%. After filtering, we identify 3,979 vulnerable DICOM deployments, all of which lack encryption and accept unauthenticated connections; 1,782 run outdated or deprecated software, and 1,551 carry known remotely exploitable vulnerabilities. Follow-up scans reveal that approximately 50% of exposed services show no evidence of maintenance over 5 months of observation, and that responsible disclosure led to only modest, short-term remediation. On the passive measurement, we deploy Dicompot and find that its raw session logs overstate activity by up to 83% due to generic TCP noise being incorrectly logged as DICOM sessions. After filtering, we observe a reconnaissance gap: Most actors issuing C-ECHO probes never escalate to data exfiltration, consistent with sophisticated actors fingerprinting the honeypot and disengaging before proceeding. Our results show that DICOM exposure measurements can be significantly distorted by deception and logging artifacts, but once corrected, reveal widespread risks to patient privacy and healthcare security that existing deception systems are insufficient to capture.

cs.CR

MemeChain: A Multimodal Cross-Chain Dataset for Meme Coin Forensics and Risk Analysis

The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts required for comprehensive risk modeling. To address this gap, we introduce MemeChain, a large-scale, open-source, cross-chain dataset comprising 34,988 meme coins across Ethereum, BNB Smart Chain, Solana, and Base. MemeChain integrates on-chain data with off-chain artifacts, including website HTML source code, token logos, and linked social media accounts, enabling multimodal and forensic study of meme coin projects. Analysis of the dataset shows that visual branding is frequently omitted in low-effort deployments, and many projects lack a functional website. Moreover, we quantify the ecosystem's extreme volatility, identifying 1,801 tokens (5.15%) that cease all trading activity within just 24 hours of launch. By providing unified cross-chain coverage and rich off-chain context, MemeChain serves as a foundational resource for research in financial forensics, multimodal anomaly detection, and automated scam prevention in the meme coin ecosystem.

cs.CR

A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem

From viral jokes to a billion-dollar phenomenon, meme coins have become one of the most popular segments in cryptocurrency markets. Unlike utility-focused crypto assets like Bitcoin, meme coins derive value primarily from community sentiment, making them vulnerable to manipulation. This study presents an unprecedented cross-chain analysis of the meme coin ecosystem, examining 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base. We characterize their tokenomics and track their growth in a three-month longitudinal analysis. We discover that among high-return tokens (>100%), an alarming 82.8% show evidence of artificial growth strategies designed to create a misleading appearance of market interest. These include wash trading and a new form of manipulation we define as Liquidity Pool-Based Price Inflation (LPI), where small strategic purchases trigger dramatic price increases. We find that profit extraction schemes, such as pump and dumps and rug pulls, typically follow initial manipulations like wash trading or LPI, indicating how early manipulations create the foundation for later exploitation. We quantify the economic impact of these schemes, identifying over 17,000 victimized addresses with realized losses exceeding $9.3 million. These findings reveal that combined manipulations are widespread among high-performing meme coins, suggesting that their dramatic gains are often driven by coordinated efforts rather than natural market dynamics.

q-fin.TR

The Conspiracy Money Machine: Uncovering Telegram's Conspiracy Channels and their Profit Model

In recent years, major social media platforms have implemented increasingly strict moderation policies, resulting in bans and restrictions on conspiracy theory-related content. To circumvent these restrictions, conspiracy theorists are turning to alternatives, such as Telegram, where they can express and spread their views with fewer limitations. Telegram offers channels, virtual rooms where only administrators can broadcast messages, and a more permissive content policy. These features have created the perfect breeding ground for a complex ecosystem of conspiracy channels. In this paper, we illuminate this ecosystem. First, we propose an approach to detect conspiracy channels. Then, we discover that conspiracy channels can be clustered into four distinct communities comprising over 17,000 channels. Finally, we uncover the "Conspiracy Money Machine," revealing how most conspiracy channels actively seek to profit from their subscribers. We find conspiracy theorists leverage e-commerce platforms to sell questionable products or lucratively promote them through affiliate links. Moreover, we observe that conspiracy channels use donation and crowdfunding platforms to raise funds for their campaigns. We determine that this business involves hundreds of thousands of donors and generates a turnover of almost $71 million.

cs.CY

Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements

Telegram is one of the most used instant messaging apps worldwide. Some of its success lies in providing high privacy protection and social network features like the channels -- virtual rooms in which only the admins can post and broadcast messages to all its subscribers. However, these same features contributed to the emergence of borderline activities and, as is common with Online Social Networks, the heavy presence of fake accounts. Telegram started to address these issues by introducing the verified and scam marks for the channels. Unfortunately, the problem is far from being solved. In this work, we perform a large-scale analysis of Telegram by collecting 35,382 different channels and over 130,000,000 messages. We study the channels that Telegram marks as verified or scam, highlighting analogies and differences. Then, we move to the unmarked channels. Here, we find some of the infamous activities also present on privacy-preserving services of the Dark Web, such as carding, sharing of illegal adult and copyright protected content. In addition, we identify and analyze two other types of channels: the clones and the fakes. Clones are channels that publish the exact content of another channel to gain subscribers and promote services. Instead, fakes are channels that attempt to impersonate celebrities or well-known services. Fakes are hard to identify even by the most advanced users. To detect the fake channels automatically, we propose a machine learning model that is able to identify them with an accuracy of 86%. Lastly, we study Sabmyk, a conspiracy theory that exploited fakes and clones to spread quickly on the platform reaching over 1,000,000 users.

cs.CY

TGDataset: Collecting and Exploring the Largest Telegram Channels Dataset

Telegram is one of the most popular instant messaging apps in today's digital age. In addition to providing a private messaging service, Telegram, with its channels, represents a valid medium for rapidly broadcasting content to a large audience (COVID-19 announcements), but, unfortunately, also for disseminating radical ideologies and coordinating attacks (Capitol Hill riot). This paper presents the TGDataset, a new dataset that includes 120,979 Telegram channels and over 400 million messages, making it the largest collection of Telegram channels to the best of our knowledge. After a brief introduction to the data collection process, we analyze the languages spoken within our dataset and the topic covered by English channels. Finally, we discuss some use cases in which our dataset can be extremely useful to understand better the Telegram ecosystem, as well as to study the diffusion of questionable news. In addition to the raw dataset, we released the scripts we used to analyze the dataset and the list of channels belonging to the network of a new conspiracy theory called Sabmyk.

cs.CY

A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain

The Non-Fungible Token (NFT) market in the Ethereum blockchain experienced explosive growth in 2021, with a monthly trade volume reaching \$6 billion in January 2022. However, concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. Our research examines the effects of wash trading on the NFT market in Ethereum from the beginning until January 2022, using multiple approaches. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of \$3,406,110,774. We look at two ways to profit from wash trading: Artificially increasing the price of the NFT and taking advantage of the token reward systems provided by some marketplaces. Our findings show that exploiting the token reward systems of NFTMs is much more profitable (mean gain of successful operations is \$1.055M on LooksRare), more likely to succeed (more than 80% of operations), and less risky than reselling an NFT at a higher price using wash trading (50% of activities result in a loss). Our research highlights that wash trading is frequent in Ethereum and that NFTMs should implement protective mechanisms to stop such illicit behavior.

cs.CY