207 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

    Get PDF
    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

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Northeastern Illinois University, Academic Catalog 2023-2024

    Get PDF
    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability

    Get PDF
    Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods

    Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

    Get PDF
    The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics

    General Course Catalog [2022/23 academic year]

    Get PDF
    General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp

    A comprehensive survey of V2X cybersecurity mechanisms and future research paths

    Get PDF
    Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

    Get PDF
    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

    Get PDF
    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    Cryptographic Protocols for Privacy Enhancing Technologies: From Privacy Preserving Human Attestation to Internet Voting

    Get PDF
    Desire of privacy is oftentimes associated with the intention to hide certain aspects of our thoughts or actions due to some illicit activity. This is a narrow understanding of privacy, and a marginal fragment of the motivations for undertaking an action with a desired level of privacy. The right for not being subject to arbitrary interference of our privacy is part of the universal declaration of human rights (Article 12) and, above that, a requisite for our freedom. Developing as a person freely, which results in the development of society, requires actions to be done without a watchful eye. While the awareness of privacy in the context of modern technologies is not widely spread, it is clearly understood, as can be seen in the context of elections, that in order to make a free choice one needs to maintain its privacy. So why demand privacy when electing our government, but not when selecting our daily interests, books we read, sites we browse, or persons we encounter? It is popular belief that the data that we expose of ourselves would not be exploited if one is a law-abiding citizen. No further from the truth, as this data is used daily for commercial purposes: users’ data has value. To make matters worse, data has also been used for political purposes without the user’s consent or knowledge. However, the benefits that data can bring to individuals seem endless and a solution of not using this data at all seems extremist. Legislative efforts have tried, in the past years, to provide mechanisms for users to decide what is done with their data and define a framework where companies can use user data, but always under the consent of the latter. However, these attempts take time to take track, and have unfortunately not been very successful since their introduction. In this thesis we explore the possibility of constructing cryptographic protocols to provide a technical, rather than legislative, solution to the privacy problem. In particular we focus on two aspects of society: browsing and internet voting. These two events shape our lives in one way or another, and require high levels of privacy to provide a safe environment for humans to act upon them freely. However, these two problems have opposite solutions. On the one hand, elections are a well established event in society that has been around for millennia, and privacy and accountability are well rooted requirements for such events. This might be the reason why its digitalisation is something which is falling behind with respect to other acts of our society (banking, shopping, reading, etc). On the other hand, browsing is a recently introduced action, but that has quickly taken track given the amount of possibilities that it opens with such ease. We now have access to whatever we can imagine (except for voting) at the distance of a click. However, the data that we generate while browsing is extremely sensitive, and most of it is disclosed to third parties under the claims of making the user experience better (targeted recommendations, ads or bot-detection). Chapter 1 motivates why resolving such a problem is necessary for the progress of digital society. It then introduces the problem that this thesis aims to resolve, together with the methodology. In Chapter 2 we introduce some technical concepts used throughout the thesis. Similarly, we expose the state-of-the-art and its limitations. In Chapter 3 we focus on a mechanism to provide private browsing. In particular, we focus on how we can provide a safer, and more private way, for human attestation. Determining whether a user is a human or a bot is important for the survival of an online world. However, the existing mechanisms are either invasive or pose a burden to the user. We present a solution that is based on a machine learning model to distinguish between humans and bots that uses natural events of normal browsing (such as touch the screen of a phone) to make its prediction. To ensure that no private data leaves the user’s device, we evaluate such a model in the device rather than sending the data over the wire. To provide insurance that the expected model has been evaluated, the user’s device generates a cryptographic proof. However this opens an important question. Can we achieve a high level of accuracy without resulting in a noneffective battery consumption? We provide a positive answer to this question in this work, and show that a privacy-preserving solution can be achieved while maintaining the accuracy high and the user’s performance overhead low. In Chapter 4 we focus on the problem of internet voting. Internet voting means voting remotely, and therefore in an uncontrolled environment. This means that anyone can be voting under the supervision of a coercer, which makes the main goal of the protocols presented to be that of coercionresistance. We need to build a protocol that allows a voter to escape the act of coercion. We present two proposals with the main goal of providing a usable, and scalable coercion resistant protocol. They both have different trade-offs. On the one hand we provide a coercion resistance mechanism that results in linear filtering, but that provides a slightly weaker notion of coercion-resistance. Secondly, we present a mechanism with a slightly higher complexity (poly-logarithmic) but that instead provides a stronger notion of coercion resistance. Both solutions are based on a same idea: allowing the voter to cast several votes (such that only the last one is counted) in a way that cannot be determined by a coercer. Finally, in Chapter 5, we conclude the thesis, and expose how our results push one step further the state-of-the-art. We concisely expose our contributions, and describe clearly what are the next steps to follow. The results presented in this work argue against the two main claims against privacy preserving solutions: either that privacy is not practical or that higher levels of privacy result in lower levels of security.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Agustín Martín Muñoz.- Secretario: José María de Fuentes García-Romero de Tejada.- Vocal: Alberto Peinado Domíngue
    corecore