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 flow that would otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC analysts rely on these systems to make decisions about the detected threats. However, IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This creates a barrier for CSoC analysts, as they are unable to improve their decisions based on the model’s predictions. One solution to this problem is to design explainable IDS (X-IDS). This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS. In particular, we discuss black box and white box approaches comprehensively. We also present the tradeoff between these approaches in terms of their performance and ability to produce explanations. Furthermore, we propose a generic architecture that considers human-in-the-loop which can be used as a guideline when designing an X-IDS. Research recommendations are given from three critical viewpoints: the need to define explainability for IDS, the need to create explanations tailored to various stakeholders, and the need to design metrics to evaluate explanations.

Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect or find patterns indicative of a cyber-attack. In this paper, we describe a system to extract information from AARs, aggregate the extracted information by fusing similar entities together, and represent that extracted information in a Cybersecurity Knowledge Graph (CKG). We extract entities by building a customized named entity recognizer called ‘Malware Entity Extractor’ (MEE). We then build a neural network to predict how pairs of ‘malware entities’ are related to each other. When we have predicted entity pairs and the relationship between them, we assert the ‘entity-relationship set’ in a CKG. Our next step in the process is to fuse similar entities, to improve our CKG. This fusion helps represent intelligence extracted from multiple documents and reports. The fused CKG has knowledge from multiple AARs, with relationships between entities extracted from separate reports. As a result of this fusion, a security analyst can execute queries and retrieve better answers on the fused CKG, than a knowledge graph with no fusion. We also showcase various reasoning capabilities that can be leveraged by a security analyst using our fused CKG.

Cyber-Physical Systems (CPS) and Internet of Thing (IoT) generate large amounts of data spurring the rise of Artificial Intelligence (AI) based smart applications. Driven by rapid advancements in technologies that support smart devices, agriculture and farming sector is shifting towards IoT connected ecosystem to balance the increase in demand for food supply. As the number of smart farms reach critical mass, it is now possible to include AI assisted systems at a cooperative (co-op) farming level. Today, in the United States alone there are about 1,871 co-ops serving 1,890,057 member farmers. Hence, such advanced technologies and infrastructure when incorporated in the co-op farming ecosystem can immensely benefit small member farmers who operate and maintain these independent co-op entities. In this paper, we develop a connected cooperative ecosystem which defines sensors and their communication among different entities along with cloud supported co-op hub. We develop member farm and co-op ontologies to capture data and various interactions that happen between shared resources, member farms, and the co-op that are stored in the cloud. These can then help generate AI supported insights for farmers and the cooperative. Several co-op farming use case scenarios have been discussed to demonstrate the functioning of our smart cooperative ecosystem. Finally, the paper describes various AI applications that can be deployed at the co-op level to aid member farmers.

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning , and control . However, while the integration of AI in Robotics systems has enhanced the quality of our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that makeup AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns , and Human-Robot Interaction (HRI) security . Our goal is to provide readers, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by identifying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field.

Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in AI systems. This approach addresses a key criticism of current generation systems, namely, their inability to generate human-understandable explanations for their outcomes and ensure safe behaviors, especially in scenarios with unknown unknowns (e.g., cybersecurity, privacy). The integration of neural networks, which excel at exploring complex data spaces, and symbolic KGs, which represent domain knowledge, allows AI systems to reason, learn, and generalize in a manner understandable to experts. This article describes how applications in cybersecurity and privacy, two of the most demanding domains in terms of the need for AI to be explainable while being highly accurate in complex environments, can benefit from neurosymbolic AI.