The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.
Context. A Smart city is intended as a city able to offer advanced integrated services, based on information and communication technology (ICT) technologies and intelligent (smart) use of urban infrastructures for improving the quality of life of its citizens. This goal is pursued by numerous cities worldwide, through smart projects that should contribute to the realization of an integrated vision capable of harmonizing the technologies used and the services developed in various application domains on which a Smart city operates. However, the current scenario is quite different. The projects carried out are independent of each other, often redundant in the services provided, unable to fully exploit the available technologies and reuse the results already obtained in previous projects. Each project is more like a silo than a brick that contributes to the creation of an integrated vision. Therefore, reference models and managerial practices are needed to bring together the efforts in progress towards a shared, integrated, and intelligent vision of a Smart city. Objective. Given these premises, the goal of this research work is to propose a Smart City Integrated Model together with a Smart Program Management approach for managing the interdependencies between project, strategy, and execution, and investigate the potential benefits that derive from using them. Method. Starting from a Smart city worldwide analysis, the Italian scenario was selected, and we carried out a retrospective analysis on a set of 378 projects belonging to nine different Italian Smart cities. Each project was evaluated according to three different perspectives: application domain transversality, technological depth, and interdependences. Results. The results obtained show that the current scenario is far from being considered “smart” and motivates the adoption of a Smart integrated model and Smart program management in the context of a Smart city. Conclusions. The development of a Smart city requires the use of Smart program management, which may significantly improve the level of integration between the application domain transversality and technological depth.
The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.
The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X–Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF–K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages.
The Internet of Things becomes Internet of Everything when in the process of communication machine-to-machine also intelligent forms of communication between human and machine are involved. Cities can be viewed as a microcosm of this interconnected system where ICT and emerging technologies can be enabling factors to transform cities in Smart Cities. Cities can take great advantage by using information intelligence to achieve important public-policy goals and, in particular, by enabling network communication channels between citizens and public administrators in order to provide information and online services in real time through platform systems rather than by means of humans, using Artificial Intelligence and Natural Language Processing techniques. This work was the first step of a wider project aimed at providing a Spell Checking Web Service API for Smart City communication platforms able to automatically select, among the large availability of open source spell checking tools, the most suitable tool based on the semantic structure of the specific textual data. The system should manage an enhanced Italian Vocabulary Database, specifically implemented to support all the tools of the system. The goal of the present work was to test, through an experimental research, the feasibility of the entire project by implementing a Spell Checking Prototype System designed to manage two selected spell checking tools. Results showed that the Spell Checking Prototype System significantly improves performances by allowing the user to select the most suitable tool for the specific semantic structure of the text. The system also enables to manage the list of exceptions, which continuously enhance the Italian Vocabulary Database. The experimentation proved scientific evidence of the validity of the project aimed at implementing a Spell Checking Web Service API in order to improve the quality of natural language data to be stored or processed in Smart City NCeSDP systems, through the use of existing spell checking tools.
In this paper the authors study the sample behavior of the Gini's index of dissimilarity in the case of two samples of equal size drawn from the same uniform population. The paper present the analytical results obtained for the exact distribution of the index of dissimilarity for sample sizes n ≤ 8. This result was obtained by expressing the index of dissimilarity as a linear combination of spacings of the pooled sample. The obtained results allow to achieve the exact expressions of the moments for any sample size and, therefore, to highlight the main features of the sampling distributions of the index of dissimilarity. The present study can enhance inferential statistical aspects about one of the main contributions of Gini.
The most famous Black-Scholes model is based on the assumption that the log-returns of financial data follow a normal distribution. Several studies performed show empirical evidence against such normality since the log-returns of most financial data show a significant leptokurtosis. The Meixner distribution is an infinitely divisible distribution and therefore a Lévy process can be associated with it, which is called the Meixner process. The Meixner process because of its simple and extreme flexible structure was proposed as a model for representing efficiently the empirical distributions of the log-returns of financial data. In this paper we studied the dynamics of the uSD/EuR exchange rates. After testing that the normal distribution provides a poor fit to the log-returns of the exchange rates, we applied the Meixner model fitting its underlying distribution to the data. performing a number of statistical tests we showed that the Meixner distribution provides an almost perfect fit to the data.
In the present paper we derived, with direct method, the exact expressions for the sampling probability density function of the Gini concentration ratio for samples from a uniform population of size n = 6, 7, 8, 9 and 10. Moreover, we found some regularities of such distributions valid for any sample size.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.