3,251 research outputs found
A Holistic Analysis of Internet of Things (IoT) Security : Principles, Practices, and New Perspectives
Peer reviewedPublisher PD
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Website Phishing Detection Using Machine Learning Techniques
Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
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The impact of enterprise social networking on knowledge sharing between academic staff in higher education
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHigher education institutions have always considered knowledge sharing critical for research excellence and finding proper methods for sharing knowledge across academic staff has therefore been a major issue for universities and knowledge management research. Recent evidence shows that many universities have embraced enterprise social networking tools to improve communication, relationships, partnerships, and knowledge sharing. To date, there is little understanding of the critical factors for online knowledge sharing behaviour between academic staff, and the impact of these factors on work benefits for academic staff which differ between consumptive users and contributive users in higher education. This study employed the extended unified theory of acceptance and use of technology (UTAUT) to examine factors affecting knowledge sharing about the consumptive use and contributive use of enterprise social network (ESN) behaviour. The study adopts a critical realism philosophical approach and employed a grounded theory mixed methods. The conceptual model was validated through structural equation modelling based on an online survey of 254 academic staff using enterprise social networking as a part of their work in the United Kingdom. The findings have significant theoretical and practical implications for researchers and policy makers. The research has developed a cohesive ESN use model by extending and modifying the unified theory of acceptance and use of technology. The findings indicate significant differences around factors affecting consumptive and contributive usage patterns within ESNs. Due to advances in communication technologies, this research argues that a previous model suggested by Venkatesh et al. (2003) is no longer fit for purpose and the new communication tools can lead to improved knowledge in higher education. This research also makes valuable contributions to universities from a managerial viewpoint, suggesting that universities could help their scholars find a more comprehensive range of funding sources matching scholars' ideas
La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review
Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article
Deteção de intrusões de rede baseada em anomalias
Dissertação de mestrado integrado em Eletrónica Industrial e ComputadoresAo longo dos últimos anos, a segurança de hardware e software tornou-se uma grande preocupação. À medida
que a complexidade dos sistemas aumenta, as suas vulnerabilidades a sofisticadas técnicas de ataque têm
proporcionalmente escalado. Frequentemente o problema reside na heterogenidade de dispositivos conectados ao
veículo, tornando difícil a convergência da monitorização de todos os protocolos num único produto de segurança.
Por esse motivo, o mercado requer ferramentas mais avançadas para a monitorizar ambientes críticos à vida
humana, tais como os nossos automóveis.
Considerando que existem várias formas de interagir com os sistemas de entretenimento do automóvel como
o Bluetooth, o Wi-fi ou CDs multimédia, a necessidade de auditar as suas interfaces tornou-se uma prioridade,
uma vez que elas representam um sério meio de aceeso à rede interna do carro. Atualmente, os mecanismos de
segurança de um carro focam-se na monitotização da rede CAN, deixando para trás as tecnologias referidas e não
contemplando os sistemas não críticos. Como exemplo disso, o Bluetooth traz desafios diferentes da rede CAN,
uma vez que interage diretamente com o utilizador e está exposto a ataques externos.
Uma abordagem alternativa para tornar o automóvel num sistema mais robusto é manter sob supervisão as
comunicações que com este são estabelecidas. Ao implementar uma detecção de intrusão baseada em anomalias,
esta dissertação visa analisar o protocolo Bluetooth no sentido de identificar interações anormais que possam
alertar para uma situação fora dos padrões de utilização. Em última análise, este produto de software embebido
incorpora uma grande margem de auto-aprendizagem, que é vital para enfrentar quaisquer ameaças desconhecidas
e aumentar os níveis de segurança globais. Ao longo deste documento, apresentamos o estudo do problema seguido
de uma metodologia alternativa que implementa um algoritmo baseado numa LSTM para prever a sequência de
comandos HCI correspondentes a tráfego Bluetooth normal. Os resultados mostram a forma como esta abordagem
pode impactar a deteção de intrusões nestes ambientes ao demonstrar uma grande capacidade para identificar padrões anómalos no conjunto de dados considerado.In the last few years, hardware and software security have become a major concern. As the systems’ complexity
increases, its vulnerabilities to several sophisticated attack techniques have escalated likewise. Quite often, the
problem lies in the heterogeneity of the devices connected to the vehicle, making it difficult to converge the monitoring
systems of all existing protocols into one security product. Thereby, the market requires more refined tools to monitor
life-risky environments such as personal vehicles.
Considering that there are several ways to interact with the car’s infotainment system, such as Wi-fi, Bluetooth,
or CD player, the need to audit these interfaces has become a priority as they represent a serious channel to reach
the internal car network. Nowadays, security in car networks focuses on CAN bus monitoring, leaving behind the
aforementioned technologies and not contemplating other non-critical systems. As an example of these concerns,
Bluetooth brings different challenges compared to CAN as it interacts directly with the user, being exposed to external
attacks.
An alternative approach to converting modern vehicles and their set of computers into more robust systems
is to keep track of established communications with them. By enforcing anomaly-based intrusion detection this
dissertation aims to analyze the Bluetooth protocol to identify abnormal user interactions that may alert for a non conforming pattern. Ultimately, such embedded software product incorporates a self-learning edge, which is vital to
face newly developed threats and increasing global security levels. Throughout this document, we present the study
case followed by an alternative methodology that implements an LSTM based algorithm to predict a sequence of
HCI commands corresponding to normal Bluetooth traffic. The results show how this approach can impact intrusion
detection in such environments by expressing a high capability of identifying abnormal patterns in the considered
data
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