Emanuele Viterbo

Brendon McBain, Yi Hong, Emanuele Viterbo

Existing analyses of ergodic capacity in satellite mega-constellations often rely on restrictive serving time assumptions or become intractable under realistic handover strategies. This paper develops a framework for characterising the ergodic capacity of low-Earth-orbit (LEO) mega-constellation links under arbitrary handover strategies and serving times. The user--satellite link is modelled as shadowed-Rician fading, and a semi-stochastic satellite channel with persistence is introduced in which visible satellites are drawn from a non-homogeneous binomial point process (NBPP) at each handover and the selected satellite is then propagated using circular orbit dynamics. Under uncoordinated handover decisions, this yields independent serving periods and enables a renewal-theoretic derivation of persistent capacity. This capacity is related to the non-persistent capacity from prior work, and closed-form bounds are provided for efficient evaluation. Optimal handover is then formulated as a non-linear fractional program, yielding an explicit decision rule via a variant of Dinkelbach's algorithm. The results show that a simpler strategy that maximises serving capacity closely approximates the optimum while performing best under SGP4-based orbit prediction and mega-constellation simulation.

Phuong Duy Huynh, Son Hoang Dau, Xiaodong Li et al.

The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum blockchain, causing considerable financial losses to many crypto investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code. This contract-code-based approach, while achieving very high accuracy, is not robust because a Ponzi developer can fool a detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected. On the contrary, a transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. In this paper, we aim to improve the accuracy of the transaction-based models by employing time-series features, which turn out to be crucial in capturing the life-time behaviour a Ponzi application but were completely overlooked in previous works. We propose a new set of 85 features (22 known account-based and 63 new time-series features), which allows off-the-shelf machine learning algorithms to achieve up to 30% higher F1-scores compared to existing works.

Li Wan, Tansu Alpcan, Margreta Kuijper et al.

We propose a novel, lightweight supervised dictionary learning framework for text classification based on data compression and representation. This two-phase algorithm initially employs the Lempel-Ziv-Welch (LZW) algorithm to construct a dictionary from text datasets, focusing on the conceptual significance of dictionary elements. Subsequently, dictionaries are refined considering label data, optimizing dictionary atoms to enhance discriminative power based on mutual information and class distribution. This process generates discriminative numerical representations, facilitating the training of simple classifiers such as SVMs and neural networks. We evaluate our algorithm's information-theoretic performance using information bottleneck principles and introduce the information plane area rank (IPAR) as a novel metric to quantify the information-theoretic performance. Tested on six benchmark text datasets, our algorithm competes closely with top models, especially in limited-vocabulary contexts, using significantly fewer parameters. \review{Our algorithm closely matches top-performing models, deviating by only ~2\% on limited-vocabulary datasets, using just 10\% of their parameters. However, it falls short on diverse-vocabulary datasets, likely due to the LZW algorithm's constraints with low-repetition data. This contrast highlights its efficiency and limitations across different dataset types.

Shuiyin Liu, Yi Hong, Emanuele Viterbo

The Unshared Secret Key Cryptography (USK), recently proposed by the authors, guarantees Shannon's ideal secrecy and perfect secrecy for MIMO wiretap channels, without requiring secret key exchange. However, the requirement of infinite constellation inputs limits its applicability to practical systems. In this paper, we propose a practical USK scheme using finite constellation inputs. The new scheme is based on a cooperative jamming technique, and is valid for the case where the eavesdropper has more antennas than the transmitter. We show that Shannon's ideal secrecy can be achieved with an arbitrarily small outage probability.

Shuiyin Liu, Yi Hong, Emanuele Viterbo

Current security techniques can be implemented with either secret key exchange or physical layer wiretap codes. In this work, we investigate an alternative solution for MIMO wiretap channels. Inspired by the artificial noise (AN) technique, we propose the unshared secret key (USK) cryptosystem, where the AN is redesigned as a one-time pad secret key aligned within the null space between transmitter and legitimate receiver. The proposed USK cryptosystem is a new physical layer cryptographic scheme, obtained by combining traditional network layer cryptography and physical layer security. Unlike previously studied artificial noise techniques, rather than ensuring non-zero secrecy capacity, the USK is valid for an infinite lattice input alphabet and guarantees Shannon's ideal secrecy and perfect secrecy, without the need of secret key exchange. We then show how ideal secrecy can be obtained for finite lattice constellations with an arbitrarily small outage.