A Systematic Survey: Security Threats to UAV-Aided IoT Applications, Taxonomy, Current Challenges and Requirements With Future Research Directions

Adil, Muhammad; Jan, Mian Ahmad; Liu, Yongxin; Abulkasim, Hussein; Farouk, Ahmed; Song, Houbing

doi:10.1109/tits.2022.3220043

Cited by 10 publications

(6 citation statements)

References 161 publications

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“… Edge computing and Reinforcement Learning (RL) [ [30] , [31] , [32] ]: Edge computing has emerged as a promising approach for improving QoS in AIoT deployments. By bringing computing resources closer to the edge of the network, edge computing reduces latency and improves the reliability of data transmissions [ [33] , [34] , [35] ]. Furthermore, edge computing allows for real-time processing of data enabling quick decision-making and reducing the risk of downtime levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Sonavane,

Prashantha,

Nikam

et al. 2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Sonavane,

Prashantha,

Nikam

et al. 2024

Heliyon

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“…Future directions in securing AI-powered systems must address bias and fairness concerns by implementing bias detection and mitigation techniques, promoting diversity and inclusion in AI development teams, and conducting thorough impact assessments to identify and mitigate potential biases. Additionally, organizations must prioritize fairness in AI algorithms and decision-making processes to ensure equitable outcomes for all users (Adil,, et al, 2023). Building cyber resilience and adaptability is essential for securing AI-powered systems in the face of evolving cyber threats and technological advancements.…”

Section: Future Directions and Challenges In Securing Ai-powered Systemsmentioning

confidence: 99%

Cybersecurity Challenges in the Age of Ai: Theoretical Approaches and Practical Solutions

Babajide Tolulope Familoni

2024

Comput. sci. IT res. j.

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In the ever-evolving landscape of cybersecurity, the proliferation of artificial intelligence (AI) technologies introduces both promising advancements and daunting challenges. This paper explores the theoretical underpinnings and practical implications of addressing cybersecurity challenges in the age of AI. With the integration of AI into various facets of digital infrastructure, including threat detection, authentication, and response mechanisms, cyber threats have become increasingly sophisticated and difficult to mitigate. Theoretical approaches delve into understanding the intricate interplay between AI algorithms, human behavior, and adversarial tactics, elucidating the underlying mechanisms of cyber attacks and defense strategies. However, this complexity also engenders novel vulnerabilities, as AI-driven attacks leverage machine learning algorithms to evade traditional security measures, posing formidable challenges to organizations across sectors. As such, practical solutions necessitate a multifaceted approach, encompassing robust threat intelligence, adaptive defense mechanisms, and ethical considerations to safeguard against AI-driven cyber threats effectively. Leveraging AI for cybersecurity defense holds promise in enhancing detection capabilities, automating response actions, and augmenting human analysts' capabilities. Yet, inherent limitations, such as algorithmic biases, data privacy concerns, and the potential for AI-enabled attacks, underscore the need for a comprehensive risk management framework. Regulatory frameworks and industry standards play a crucial role in shaping the development and deployment of AI-powered cybersecurity solutions, ensuring accountability, transparency, and compliance with ethical principles. Moreover, fostering interdisciplinary collaboration and investing in cybersecurity education and training are vital for cultivating a skilled workforce equipped to navigate the evolving threat landscape. By integrating theoretical insights with practical strategies, this paper elucidates key challenges and opportunities in securing AI-driven systems, offering insights for policymakers, researchers, and practitioners alike. Keywords: Cybersecurity; Artificial Intelligence; Threat Detection; Defense Strategies; Ethical Considerations; Regulatory Frameworks.

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“…UAV manufacturers and service providers utilize the latest research findings to develop more powerful, intelligent, and collaborative-capable UAV systems. These products and services are widely used in agriculture, forestry, construction, logistics, and security monitoring, helping enterprises to improve operational efficiency, reduce costs, and create new value [126][127][128].…”

Section: Enterprisesmentioning

confidence: 99%

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Wang,

Huang,

et al. 2024

Processes

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In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology.

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A Systematic Survey: Security Threats to UAV-Aided IoT Applications, Taxonomy, Current Challenges and Requirements With Future Research Directions

Abstract: County (UMBC)ScholarWorks@UMBC digital repository on the Maryland Shared Open Access (MD-SOAR) platform.

Cited by 10 publications

References 161 publications

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Cybersecurity Challenges in the Age of Ai: Theoretical Approaches and Practical Solutions

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

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