Smart Grids are energy delivery networks, constituting an evolution of power grids, in which a bidirectional flow between power providers and consumers is established. These flows support the transfer of electricity and information, in order to support automation actions in the context of the energy delivery network. Insofar, many smart grid implementations and implementation proposals have emerged, with varying degrees of feature delivery and sophistication.

This paper sheds new light in the overall definition of the threat landscape that emerges by the convergence of CC and IoT in a SG.

Cybersecurity is a growing concern for private individuals and professional entities. Reports have shown that the majority of cybersecurity incidents occur because users fail to behave securely. Research on human cybersecurity (HCS) behavior suggests that time pressure is one of the important driving factors behind non-secure HCS behavior. However, there is limited conceptual work to guide researchers and practitioners in this regard. Against this backdrop, we investigate how the impact of time pressure on HCS behavior can be conceptualized within an integrative framework and which countermeasures can be used to reduce its negative impact. Altogether, we conducted 35 interviews with cybersecurity experts, non-security professionals, and private users.

A “national security–centric” approach currently dominates cybersecurity policies and practices. Derived from a realist theory of world politics in which states compete with each other for survival and relative advantage, the principal cybersecurity threats are conceived as those affecting sovereign states, such as damage to critical infrastructure within their territorial jurisdictions. As part of a roundtable on “Competing Visions for Cyberspace,” this essay presents an alternative approach to cybersecurity that is derived from the tradition of “human security.”

This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in the Internet of Things (IoT). The cybersecurity of emerging technologies such as smart cities is also discussed. In addition, associated solutions based on artificial intelligence and machine learning frameworks to prevent cyber-risks are also discussed. Our review will serve as a reference for policy-makers from the industry, government, and the cybersecurity research community.

The major trends and transformations in energy systems have brought many challenges, and cybersecurity and operational security are among the most important issues to consider. First, due to the criticality of the energy sector. Second, due to the lack of smart girds’ cybersecurity professionals. Previous research has highlighted skill gaps and shortage in cybersecurity training and education in this sector. Accordingly, we proceeded by crafting a roadmap strategy to foster cybersecurity education in smart grids.

In this article, we aim to provide an important step to progress the AI for Cybersecurity discipline. We first provide an overview of prevailing cybersecurity data, summarize extant AI for Cybersecurity application areas, and identify key limitations in the prevailing landscape. Based on these key issues, we offer a multi-disciplinary AI for Cybersecurity roadmap that centers on major themes such as cybersecurity applications and data, advanced AI methodologies for cybersecurity, and AI-enabled decision making. To help scholars and practitioners make significant headway in tackling these grand AI for Cybersecurity issues, we summarize promising funding mechanisms from the National Science Foundation (NSF) that can support long-term, systematic research programs. We conclude this article with an introduction of the articles included in this special issue.

This article addresses the role that US service academies play in developing not only future cyber forces, but also a pipeline of qualified cyber-strategic military leaders, who have the knowledge necessary to confront a wide array of cyber threats and establish both a competitive and security advantage in the modern battlespace. In the future, every military leader must be a cyber-strategic leader. In particular, this study surveys current efforts by the US Coast Guard Academy, the US Air Force Academy, the US Military Academy, and the US Naval Academy to prepare all their future officers for the challenges of operational– and strategic–level leadership in an age of persistent cyber threat.

Given the enterprise technology corporation technological learning problem of challenging to reduce the efficiency of technology transfer based on evolutionary game theory, from the nature of technical knowledge, military field, and civilian field of technology spillover, recessive and technical complexity, and other technical features, construction of bounded rationality under the condition of the private enterprise, the enterprise, and the government's three evolutionary game models, the influence of different technical characteristics on the three parties is analyzed by Matlab numerical simulation.

As unmanned aerial vehicles (UAVs) become increasingly integrated across various domains, both military and civilian, safeguarding the security of their navigation systems becomes paramount. In the contemporary age, the prominence of cybersecurity for UAVs has grown due to a rising number of cyberattacks on these systems. Notably, over the past decade, several significant cybersecurity breaches have impacted UAVs due to inadequate vulnerability assessments and security measures. Deep learning (DL)-based algorithms show immense potential for enabling autonomous UAV navigation. However, these algorithms are susceptible to malicious attacks, such as DL-based Trojan attacks, which can compromise the integrity and reliability of UAV navigation systems. This paper addresses potential vulnerabilities in DL-based UAV navigation systems and emphasizes the importance of securing these systems against DL-based Trojan attacks. We design various trigger patterns for collision and steering angle of the DroNet model incorporating adversarial inputs to test the robustness of the deep learning algorithm used for UAV navigation. By simulating potential attacks and studying their effects, we aim to highlight the weaknesses and potential entry points for malicious interference. We assess the effectiveness of Trojan attacks on the DroNet model using poisoned collision and steering angle datasets. Subsequently, we regulate the intensity of the designed triggers and evaluate the performance of the DroNet architecture. Additionally, we propose mitigation strategies to enhance the robustness and security of navigation systems against these attacks. To identify the likelihood of the trained model being trojaned or not, we have developed a Trojan detector and created distinct DroNet Trojan Model Datasets for this purpose. That the DroNet model is vulnerable to DL-based Trojan attacks, as evidenced by the successful manipulation of collision and steering angle predictions. The Trojan detector effectively identifies potential compromises within the model, highlighting the necessity for enhanced security measures. © 2024 The Authors.