334 research outputs found
Overview of RIS-Enabled Secure Transmission in 6G Wireless Networks
As sixth-generation (6G) wireless communication networks evolve, privacy
concerns are expected due to the transmission of vast amounts of
security-sensitive private information. In this context, a reconfigurable
intelligent surface (RIS) emerges as a promising technology capable of
enhancing transmission efficiency and strengthening information security. This
study demonstrates how RISs can play a crucial role in making 6G networks more
secure against eavesdropping attacks. We discuss the fundamentals, and
standardization aspects of RISs, along with an in-depth analysis of
physical-layer security (PLS). Our discussion centers on PLS design using RIS,
highlighting aspects like beamforming, resource allocation, artificial noise,
and cooperative communications. We also identify the research issues, propose
potential solutions, and explore future perspectives. Finally, numerical
results are provided to support our discussions and demonstrate the enhanced
security enabled by RIS.Comment: Accepted for Digital Communications and Networks(DCN
Time-Range FDA Beampattern Characteristics
Current literature show that frequency diverse arrays (FDAs) are able of
producing range-angle-dependent and time-variant transmit beampatterns, but the
resulting time and range dependencies and their characteristics are still not
well understood. This paper examines the FDA transmission model and the model
for the FDA array factor, considering their time-range relationship. We develop
two novel FDA transmit beampatterns, both yielding the auto-scanning capability
of the FDA transmit beams. The scan speed, scan volume, and initial mainlobe
direction of the beams are also analyzed. In addition, the equivalent
conditions for the FDA integral transmit beampattern and the multiple-input
multiple-output (MIMO) beampattern are investigated. Various numerical
simulations illustrate the auto-scanning property of the FDA beampattern and
the proposed equivalent relationship with the MIMO beampattern, providing the
basis for an improved understanding and design of the FDA transmit beampattern.Comment: 10 pages, 9 figure
How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems
Multi-agent cyberphysical systems enable new capabilities in efficiency,
resilience, and security. The unique characteristics of these systems prompt a
reevaluation of their security concepts, including their vulnerabilities, and
mechanisms to mitigate these vulnerabilities. This survey paper examines how
advancement in wireless networking, coupled with the sensing and computing in
cyberphysical systems, can foster novel security capabilities. This study
delves into three main themes related to securing multi-agent cyberphysical
systems. First, we discuss the threats that are particularly relevant to
multi-agent cyberphysical systems given the potential lack of trust between
agents. Second, we present prospects for sensing, contextual awareness, and
authentication, enabling the inference and measurement of ``inter-agent trust"
for these systems. Third, we elaborate on the application of quantifiable trust
notions to enable ``resilient coordination," where ``resilient" signifies
sustained functionality amid attacks on multiagent cyberphysical systems. We
refer to the capability of cyberphysical systems to self-organize, and
coordinate to achieve a task as autonomy. This survey unveils the cyberphysical
character of future interconnected systems as a pivotal catalyst for realizing
robust, trust-centered autonomy in tomorrow's world
Examining Unmanned Aerial System Threats & Defenses: A Conceptual Analysis
The integration of unmanned aerial systems (UAS) into the already complex global aviation system presents new and unique hazards. While many studies have addressed the potential safety concerns of UAS integration, little research has been dedicated to the potential security implications. This study sought to identify potential uses and adaptations of civil UAS systems as weapons of terrorism or crime and potential UAS defenses. Researchers examined 68 academic studies, unclassified government reports, and news articles using Conceptual Analysis to systematically capture and categorize various threats. Using the collected data, researchers developed a UAS threat model for categorically evaluating potential threats. Evaluating UAS defense methodologies, researchers developed a five-layer, defense-in-depth model for protecting assets and individuals from UAS threats
Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid
The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models
DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)
Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
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-Robotics systems has enhanced the quality our lives, it has also presented a
serious problem - these systems are vulnerable to security attacks. The
physical components, algorithms, and data that make up 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 users, developers and other
stakeholders with a holistic understanding of these areas to enhance the
overall AI-Robotics system security. We begin by surveying 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
Secure Data Collection and Analysis in Smart Health Monitoring
Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms.
In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks
- …