Catálogo de publicaciones - revistas

<jats:p>Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. To this end, deep learning-based anomaly detection (DLAD) methods have been proposed. In this article, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation, and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. Also, we discuss the limitations and open problems of these methods. Moreover, to give users insights into choosing proper DLAD methods in practice, we experimentally explore the characteristics of typical neural models, the workflow of DLAD methods, and the running performance of DL models. Finally, we discuss the deficiencies of DL approaches, our findings, and possible directions to improve DLAD methods and motivate future research.</jats:p>

<jats:p>Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.</jats:p>

<jats:p>Recommender Systems (RS) have proven to be effective tools to help users overcome information overload, and significant advances have been made in the field over the past two decades. Although addressing the recommendation problem required first a formulation that could be easily studied and evaluated, there currently exists a gap between research contributions and industrial applications where RS are actually deployed. In particular, most RS are meant to function in batch: they rely on a large static dataset and build a recommendation model that is only periodically updated. This functioning introduces several limitations in various settings, leading to considering more realistic settings where RS learn from continuous streams of interactions. Such RS are framed as Stream-Based Recommender Systems (SBRS).</jats:p> <jats:p>In this article, we review SBRS, underline their relation with time-aware RS and online adaptive learning, and present and categorize existing work that tackle the corresponding problem and its multiple facets. We discuss the methodologies used to evaluate SBRS and the adapted datasets that can be used, and finally we outline open challenges in the area.</jats:p>

<jats:p>With the advances of chip manufacturing technologies, computer architects have been able to integrate an increasing number of processors and other heterogeneous components on the same chip. Network-on-Chip (NoC) is widely employed by multicore System-on-Chip (SoC) architectures to cater to their communication requirements. NoC has received significant attention from both attackers and defenders. The increased usage of NoC and its distributed nature across the chip has made it a focal point of potential security attacks. Due to its prime location in the SoC coupled with connectivity with various components, NoC can be effectively utilized to implement security countermeasures to protect the SoC from potential attacks. There is a wide variety of existing literature on NoC security attacks and countermeasures. In this article, we provide a comprehensive survey of security vulnerabilities in NoC-based SoC architectures and discuss relevant countermeasures.</jats:p>

<jats:p>In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This article comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker’ s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. To the best of our knowledge, this work is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.</jats:p>

<jats:p>Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.</jats:p>

<jats:p>Computational generation of stories is a subfield of computational creativity where artificial intelligence and psychology intersect to teach computers how to mimic humans’ creativity. It helps generate many stories with minimum effort and customize the stories for the users’ education and entertainment needs. Although the automatic generation of stories started to receive attention many decades ago, advances in this field to date are less than expected and suffer from many limitations. This survey presents an extensive study of research in the area of non-interactive textual story generation, as well as covering resources, corpora, and evaluation methods that have been used in those studies. It also shed light on factors of story interestingness.</jats:p>

<jats:p>Bidirectional typing combines two modes of typing: type checking, which checks that a program satisfies a known type, and type synthesis, which determines a type from the program. Using checking enables bidirectional typing to support features for which inference is undecidable; using synthesis enables bidirectional typing to avoid the large annotation burden of explicitly typed languages. In addition, bidirectional typing improves error locality. We highlight the design principles that underlie bidirectional type systems, survey the development of bidirectional typing from the prehistoric period before Pierce and Turner’s local type inference to the present day, and provide guidance for future investigations.</jats:p>

<jats:p>Internet of Things applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data protection laws. However, doing so can be a difficult and challenging task. Recent research has revealed that developers typically face difficulties when complying with regulations. One key reason is that, at times, regulations are vague and could be challenging to extract and enact such legal requirements. In this article, we have conducted a systematic analysis of the privacy and data protection laws that are used across different continents, namely (i) General Data Protection Regulations, (ii) the Personal Information Protection and Electronic Documents Act, (iii) the California Consumer Privacy Act, (iv) Australian Privacy Principles, and (v) New Zealand’s Privacy Act 1993. Then, we used framework analysis method to attain a comprehensive view of different privacy and data protection laws and highlighted the disparities to assist developers in adhering to the regulations across different regions, along with creating a Combined Privacy Law Framework (CPLF). After that, the key principles and individuals’ rights of the CPLF were mapped with Privacy by Design (PbD) schemes (e.g., privacy principles, strategies, guidelines, and patterns) developed previously by different researchers to investigate the gaps in existing schemes. Subsequently, we have demonstrated how to apply and map privacy patterns into IoT architectures at the design stage and have also highlighted the complexity of doing such mapping. Finally, we have identified the major challenges that should be addressed and potential research directions to take the burden off software developers when applying privacy-preserving techniques that comply with privacy and data protection laws. We have released a companion technical report [3] that comprises all definitions, detailed steps on how we developed the CPLF, and detailed mappings between CPLF and PbD schemes.</jats:p>

<jats:p> Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic, and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence, and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our article provides a comprehensive survey of the state of the art in the <jats:italic>assurance of ML</jats:italic> , i.e., in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the <jats:italic>machine learning lifecycle</jats:italic> , i.e., of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The article begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research. </jats:p>