Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.

Vehicular ad hoc networks (VANETs) allow vehicle-to-vehicle communication and, in particular, vehicle-generated announcements. Provided that the trustworthiness of such announcements can be guaranteed, they can greatly increase the safety of driving. A new system for vehicle-generated announcements is presented that is secure against external and internal attackers attempting to send fake messages. Internal attacks are thwarted by using an endorsement mechanism based on threshold signatures. Our system outperforms previous proposals in message length and computational cost. Three different privacy-preserving variants of the system are also described to ensure that vehicles volunteering to generate and/or endorse trustworthy announcements do not have to sacrifice their privacy.

Error-correcting codes and matroids have been widely used in the study of ordinary secret sharing schemes. In this paper, the connections between codes, matroids, and a special class of secret sharing schemes, namely, multiplicative linear secret sharing schemes (LSSSs), are studied. Such schemes are known to enable multiparty computation protocols secure against general (nonthreshold) adversaries. Two open problems related to the complexity of multiplicative LSSSs are considered in this paper. The first one deals with strongly multiplicative LSSSs. As opposed to the case of multiplicative LSSSs, it is not known whether there is an efficient method to transform an LSSS into a strongly multiplicative LSSS for the same access structure with a polynomial increase of the complexity. A property of strongly multiplicative LSSSs that could be useful in solving this problem is proved. Namely, using a suitable generalization of the well-known Berlekamp-Welch decoder, it is shown that all strongly multiplicative LSSSs enable efficient reconstruction of a shared secret in the presence of malicious faults. The second one is to characterize the access structures of ideal multiplicative LSSSs. Specifically, the considered open problem is to determine whether all self-dual vector space access structures are in this situation. By the aforementioned connection, this in fact constitutes an open problem about matroid theory, since it can be restated in terms of representability of identically self-dual matroids by self-dual codes. A new concept is introduced, the flat-partition, that provides a useful classification of identically self-dual matroids. Uniform identically self-dual matroids, which are known to be representable by self-dual codes, form one of the classes. It is proved that this property also holds for the family of matroids that, in a natural way, is the next class in the above classification: the identically self-dual bipartite matroids.

The Internet of Things is gaining momentum thanks to the provided vision of seamlessly interconnected devices. However, a unified way to discover and to interact with the surrounding smart environment is missing. As an outcome, we have been assisting to the development of heterogeneous ecosystems, where each service provider adopts its own protocol- thus preventing IoT devices from interacting when belonging to different providers. And, the same is happening again for the blockchain technology which provides a robust and trusted way to accomplish tasks -unfortunately not providing interoperability thus creating the same heterogeneous ecosystems above highlighted. In this context, the fundamental research question we address is how do we find things or services in the Internet of Things. In this paper, we propose the first IoT discovery approach which provides an answer to the above question by exploiting hierarchical and universal multi-layered blockchains. Our approach does neither define new standards nor force service providers to change their own protocol. On the contrary, it leverages the existing and publicly available information obtained from each single blockchain to have a better knowledge of the surrounding environment. The proposed approach is detailed and discussed with the support of relevant use cases.