1,720,968 research outputs found
Quality in University Education: the experience at the Department of Business Studies and Researches, of Salerno University,
No full textAn Association Rules-Based Approach for Anomaly Detection on CAN-bus
No full textWith the rapid development of the Internet of Things (IoT) and the Internet of Vehicles (IoV) technologies, smart vehicles have replaced conventional ones by providing more advanced driving-related features. IoV systems typically consist of Intra-Vehicle Networks (IVNs) in which many Electronic Control units (ECUs) directly and indirectly communicate among them through the Controller Area Network (CAN) bus. However, the growth of such vehicles has also increased the number of network and physical attacks focused on exploiting security weaknesses affecting the CAN protocol. Such problems can also endanger the life of the driver and passengers of the vehicle, as well as that of pedestrians. Therefore, to face this security issue, we propose a novel anomaly detector capable of considering the vehicle-related state over time. To accomplish this, we combine different most famous algorithms to consider all possible relationships between CAN messages and arrange them as corresponding associative rules. The presented approach, also compared with the state-of-the-art solutions, can effectively detect different kinds of attacks (DoS, Fuzzy, GEAR and RPM) by only considering CAN messages collected during attack-free operating scenarios
A federated approach to Android malware classification through Perm-Maps
Full text linkIn the last decades, mobile-based apps have been increasingly used in several application fields for many purposes involving a high number of human activities. Unfortunately, in addition to this, the number of cyber-attacks related to mobile platforms is increasing day-by-day. However, although advances in Artificial Intelligence science have allowed addressing many aspects of the problem, malware classification tasks are still challenging. For this reason, the following paper aims to propose new special features, called permission maps (Perm-Maps), which combine information related to the Android permissions and their corresponding severity levels. Such features have proven to be very effective in classifying different malware families through the usage of a convolutional neural network. Also, the advantages introduced by the Perm-Maps have been enhanced by a training process based on a federated logic. Experimental results show that the proposed approach achieves up to a 3% improvement in average accuracy with respect to J48 trees and Naive Bayes classifier, and up to 16% compared to multi-layer perceptron classifier. Furthermore, the combined use of Perm-Maps and federated logic allows dealing with unbalanced training datasets with low computational efforts
Artificial neural networks for resources optimization in energetic environment
Full text linkResource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach
An Android Malware Multi-class Classification Explained Through Genetic Programming
No full textThe interest in applying Artificial Intelligence algorithms within security contexts is rapidly growing, particularly for the tasks related to malware detection and classification. Over the last decade, numerous Machine Learning (ML) and Deep Learning (DL)-based techniques have been proposed to address the growth of malicious applications, focusing on utilizing features derived from dynamic malware analysis. However, these approaches are often considered black boxes due to their limited ability to explain the results they produce. On the contrary, this study seeks to develop a new model for identifying malware families in an interpretable manner. The methodology employs Genetic Programming to construct a multi-class classifier characterized by a mathematical formula expressing the relationship between dynamic features and the considered malware families. Experimental results, based on Android applications from Unisa Malware Dataset (UMD), showcase the effectiveness of our approach in achieving comparable average scores to the most famous Machine Learning techniques
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Effective classification of android malware families through dynamic features and neural networks
No full textDue to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, it is necessary to build tools that can reliably detect zero-day malware on these devices. At the moment, many of the frameworks that have been proposed to detect malware applications leverage Machine Learning (ML) techniques. However, an essential requirement to build these frameworks consists of using very large and sophisticated datasets for model construction and training purposes. Their success, indeed, strongly depends on the choice of the right features used for building a classification model providing adequate generalisation capability. Furthermore, the creation of a training dataset that well represents the malware properties and behaviour is one of the most critical challenges in malware analysis. Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on http://antlab.di.unisa.it/malware/, which is based on the extraction of static and dynamic features characterising the malware activities. Additionally, we will show some experiments concerning common ML tools to demonstrate how it is possible to build efficient ML-based malware classification frameworks using the proposed dataset
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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