Hamburg University of Technology（汉堡工业大学）；Leibniz Universität Hannover（汉诺威莱布尼茨大学）；Heriot-Watt University（赫瑞-瓦特大学）；Technische Universität Berlin（柏林工业大学）；University of Oldenburg（奥尔登堡大学）；Paderborn University（帕德博恩大学）；RWTH Aachen University（亚琛工业大学）
Mathias Blumreiter, Joel Greenyer, Francisco Javier Chiyah Garcia, Verena Klös, Maike Schwammberger, Christoph Sommer, Andreas Vogelsang, Andreas Wortmann
With the increasing complexity of CPSs, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the MAB-EX framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.
Cybersecurity is a domain where the data distribution is constantly changing with attackers exploring newer patterns to attack cyber infrastructure. Intrusion detection system is one of the important layers in cyber safety in today's world. Machine learning based network intrusion detection systems started showing effective results in recent years. With deep learning models, detection rates of network intrusion detection system are improved. More accurate the model, more the complexity and hence less the interpretability. Deep neural networks are complex and hard to interpret which makes diffi…
Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework
Department of Business Information Systems, Central Michigan University（中央密歇根大学商业信息系统系）；Department of Information Studies, Trine University（特林大学信息学系）；Department of Computer Science, Maharishi International University（Maharishi国际大学计算机科学系）
A plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual reality (VR). However, these detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone VR head-mounted displays (HMDs). This work presents an explainable artificial intelligence (XAI)-based framework VR-LENS for developing cybersickness detection ML models, explaining them, reducing their size, and deploying them in a Qualcom…
To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participants were free to group the given consequences of chosen cyber attacks into as many categories as they wished using rationales they see fit. The results of the open card sorting study showed a large amount of inter-participant variation making the research team wonder how the consequences of security attacks were comprehended by the participants. As an exploration of whether it is possible to explain user's mental model…
Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine Learning Framework
Information Technologies Group - atlanTTic University of Vigo（信息科技组 - atlanTTic大学维戈分校）
Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
Department of Computer Science, College of Computing and Mathematical Sciences, Khalifa University（计算机科学系，计算与数学科学学院，哈利法大学）；ECE Department, University of Toronto（电子工程系，多伦多大学）；Concordia Institute for Information Systems Engineering (CIISE), Concordia University（信息系统工程研究所（CIISE），康科迪亚大学）；Department of Electrical Engineering, College of Engineering and Physical Sciences, Khalifa University（电气工程系，工程与物理科学学院，哈利法大学）
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
Department of Information Security, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia（信息安全系，计算机科学与工程学院，哈伊尔大学，沙特阿拉伯）
Arizona State University（亚利桑那州立大学）；Institute for Computer Science and Engineering (ICIC UNS–CONICET)（计算机科学与工程研究所（ICIC UNS–CONICET））；Institute for Computer Science Research (ICC UBA–CONICET)（计算机科学研究所（ICC UBA–CONICET））；Universidad Nacional del Sur（南方国立大学）；Universidad Nacional de Entre Ríos（恩特雷里奥斯国立大学）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）
With the availability of large datasets and ever-increasing computing power, there has been a growing use of data-driven artificial intelligence systems, which have shown their potential for successful application in diverse areas. However, many of these systems are not able to provide information about the rationale behind their decisions to their users. Lack of understanding of such decisions can be a major drawback, especially in critical domains such as those related to cybersecurity. In light of this problem, in this paper we make three contributions: (i) proposal and discussion of deside…
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond
Netcompany-Intrasoft S.A.（Netcompany-Intrasoft公司）；Hellenic Mediterranean University（希腊地中海大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；University of Western Macedonia（西马其顿大学）
Mississippi State University（密西西比州立大学）；U.S Army Engineer Research and Development Center（美国陆军工程研究与发展中心）
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network…
There is a growing trend towards the convergence of cyber-physical systems (CPS) and social computing, which will lead to the emergence of smart communities composed of various objects (including both human individuals and physical things) that interact and cooperate with each other. These smart communities promise to enable a number of innovative applications and services that will improve the quality of life. This position paper addresses some opportunities and challenges of building smart communities characterized by cyber-physical and social intelligence.
Norwegian University of Science and Technology（挪威科技大学）；Old Dominion University（奥多明尼昂大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Chulalongkorn University（朱拉隆功大学）；Simula Research Laboratory（西穆拉研究实验室）
During the last two decades, distributed energy systems, especially renewable energy sources (RES), have become more economically viable with increasing market share and penetration levels on power systems. In addition to decarbonization and decentralization of energy systems, digitalization has also become very important. The use of artificial intelligence (AI), advanced optimization algorithms, Industrial Internet of Things (IIoT), and other digitalization frameworks makes modern power system assets more intelligent, while vulnerable to cybersecurity risks. This paper proposes the concept of…
