Telegram is a widely adopted instant messaging platform. It has become worldwide popular because of its emphasis on privacy and its social network features such as channels-virtual rooms in which only the admins can post and broadcast messages to all the subscribers. Channels are used to deliver live updates (e.g., weather alerts) and content to a large audience (e.g., COVID-19 announcements) but unfortunately also to disseminate radical ideologies and coordinate attacks such as the Capitol Hill riot.

In the world of cryptocurrencies, the public listing of a new token often generates significant hype. In many cases, the price of the token skyrockets in a few seconds, and timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify operations performed using sniper bots. Our findings reveal 352,413 sniping operations on Ethereum and 1,716,917 on BSC for a total turnaround of $155,630,184 and $137,548,859, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze possible countermeasures and mechanisms used in token smart contracts that can reduce the negative impact of sniper bots.

Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection. Telegram has standout social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams. In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze two types of channels: Clones and fakes. Clones are channels that publish identical content from another channel in order to gain subscribers and promote services. Fakes, on the other hand, are channels that impersonate celebrities or well-known services by posting their own messages. To automatically detect fake channels, we propose a machine learning model that achieves an F1-score of 85.45%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.

With the rise of smart working and recent global events, the risk of cyberattacks is increasing steadily. Sometimes adversaries focus on stealing valuable data, such as intellectual property (IP): they exfiltrate a large volume of IP documents from a target company. They then identify those of their interest by leveraging automated methods. This work proposes the DARD (Deceptive Approaches for Robust Defense against IP theft) system, a framework designed to deceive adversaries who rely on automatic approaches to classify exfiltrated documents. Starting from an original repository of documents, DARD automatically generates a new deceptive repository that misleads popular automatic approaches, resulting in clusters of documents that are significantly different from the actual ones. By utilizing this approach, DARD aims to hinder the accurate clustering and the identification of the topic of documents by adversaries relying on automated techniques. The paper presents four deceptive operations (Basic Shuffle, Shuffle increment, Shuffle reduction, and Change topic) that DARD leverages to create a deceptive repository. We evaluate the efficacy of our approach by considering three different types of adversaries, each possessing varying levels of knowledge and expertise. Through extensive experiments, we show that the DARD system can deceive both automatic topic modeling and document clustering techniques, including widely-used commercial tools such as Amazon Comprehend. Hence, our solution provides a robust defense mechanism against Intellectual Property (IP) theft.

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.

Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection and social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams.In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze fake channels on Telegram. To automatically detect fake channels, we propose a machine learning model that achieves an accuracy of 85.49%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.