Abnormality Detection with Rxc3xa9nyi Divergence for Univariate Gaussian Data This paper investigates the abnormality detection based on the Renyi divergence with the divergence order between 0 and 1. It is assumed that univariate Gaussian data samples are independent and identically distributed (i.i.d.). The distance between the estimated distribution from observed data and that from historical data under the normal condition is quantified by the Renyi divergence, the proposed test statistic. The false alarm rate (FAR) and the missed alarm rate (MAR) are derived analytically based on the distribution of the Renyi divergence. Under the abnormal condition, constant bias and multiplicative faults are considered. Taking both the FAR and MAR into consideration, the proposed detection algorithm can adaptively optimize the divergence order and threshold according to observed data. In the simulation, the analytical FAR and MAR results are verified by the Monte Carlo simulations, and the proposed algorithm is shown to outperform the KullbackLeibler divergence based method.

A Multiple Rxc3xa9nyi Entropy Based Intrusion Detection System for Connected Vehicles In this paper, we propose an intrusion detection system based on the estimation of the Renyi entropy with multiple orders. The Renyi entropy is a generalized notion of entropy that includes the Shannon entropy and the minentropy as special cases. In 2018, Kim proposed an efficient estimation method for the Renyi entropy with an arbitrary real order    xcexb1   . In this work, we utilize this method to construct a multiple order, Renyi entropy based intrusion detection system (IDS) for vehicular systems with various network connections. The proposed method estimates the Renyi entropies simultaneously with three distinct orders, two, three, and four, based on the controller area network (CAN)IDs of consecutively generated frames. The collected frames are split into blocks with a fixed number of frames, and the entropies are evaluated based on these blocks. For a more accurate estimation against each type of attack, we also propose a retrospective sliding window method for decision of attacks based on the estimated entropies. For fair comparison, we utilized the CANID attack data set generated by a research team from Korea University. Our results show that the proposed method can show the false negative and positive errors of less than 1% simultaneously.