Reinforcement Learning-Based Security/Safety UAV System for Intrusion Detection Under Dynamic and Uncertain Target Movement

Masadeh, Ala’eddin; Alhafnawi, Mohannad; Musa, Ahmed; Jararweh, Yaser

doi:10.1109/tem.2022.3165375

Cited by 22 publications

(6 citation statements)

References 19 publications

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“…Some of the previous studies have considered drone detection approaches as intrusion detection. The study conducted in [198] proposed an intrusion detection system by using a Markov decision process optimization problem via the RL approach. The RL-based approach was implemented in [199] regarding counter-drone technology to provide a safeguarded sky while not clashing the other objects in the neighborhood.…”

Section: Reinforcement Learning-based Approachmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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Section: Reinforcement Learning-based Approachmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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“…One of the main uses of UAVs in Smart Cities is for data collection and surveillance. In many cases, an autonomous control capability of the drone is needed so that it can maneuver in and out of enclosed locations and avoid obstacles [397]- [401]. As the number of users of wireless communication networks increases, the use of UAVs to support these networks seems promising.…”

Section: • Autonomous Navigationmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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“…Most recently, authors in [182], developed a smart UAV that has the ability to detect a dynamic intruder target. The problem of optimizing the search path of the UAV was formulated as an RL problem, where the objective is to maximize the search benefits.…”

Section: ) Joint Communication and Fight Control Aspectsmentioning

confidence: 99%