Data Mining - Concepts and Techniques.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

Machine Learning in IoT Security: Current Solutions and Future Challenges

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

Educational data mining and learning analytics: An updated survey

A survey on machine learning for data fusion

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion

Attacks over the Internet are becoming more and more complex and sophisticated. How to detect security threats and measure the security of the Internet arises a significant research topic. For detecting the Internet attacks and measuring its security, collecting different categories of data and employing methods of data analytics are essential. However, the literature still lacks a thorough review on security-related data collection and analytics on the Internet. Therefore, it becomes a necessity to review the current state of the art in order to gain a deep insight on what categories of data should be collected and which methods should be used to detect the Internet attacks and to measure its security. In this paper, we survey existing studies about security-related data collection and analytics for the purpose of measuring the Internet security. We first divide the data related to network security measurement into four categories: 1) packet-level data; 2) flow-level data; 3) connection-level data; and 4) host-level data. For each category of data, we provide a specific classification and discuss its advantages and disadvantages with regard to the Internet security threat detection. We also propose several additional requirements for security-related data analytics in order to make the analytics flexible and scalable. Based on the usage of data categories and the types of data analytic methods, we review current detection methods for distributed denial of service flooding and worm attacks by applying the proposed requirements to evaluate their performance. Finally, based on the completed review, a list of open issues is outlined and future research directions are identified.

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Xuyang Jing

Papers

A survey on machine learning for data fusion

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion

Security Data Collection and Data Analytics in the Internet: A Survey

Network traffic fusion and analysis against DDoS flooding attacks with a novel reversible sketch

Network traffic classification for data fusion: A survey