Machine Learning and Software Defined Network to secure communications in a swarm of drones

Guerber, Christophe; Royer, Mickaël; Larrieu, Nicolas

doi:10.1016/j.jisa.2021.102940

Cited by 25 publications

(8 citation statements)

References 21 publications

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“…C. Guerber et al propose an SDN solution for external threats and an RF classifier-based ML solution for intra-network attacks. As a result, they observed that the proposed system was successful in detecting abnormal behavior [13].…”

Section: A Trust Managementmentioning

confidence: 89%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.

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Section: A Trust Managementmentioning

confidence: 89%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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“…• Securing Communications in a Swarm of Drones: Another innovative application involved machine learning in conjunction with Software Defined Network (SDN) technologies to bolster the security of communication networks within drone swarms. The implemented solution employed a Random Forest Classifier-based machine learning model to detect prevalent network attacks, including denial of service, port scanning, and brute force attacks, showcasing the versatility of machine learning in diverse network scenarios [21].…”

Section: Machine Learning In Network Securitymentioning

confidence: 99%

Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security

M Sri Lakshmi

2024

jes

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The research addresses the critical anomaly detection problem in Software-Defined Networking (SDN), a domain where network integrity and security are paramount. Employing the Isolation Forest algorithm, a machine learning model renowned for its efficacy in identifying outliers, the study systematically generates synthetic network traffic data to train and test the model's detection capabilities. The methodology encompasses simulating a range of contamination rates to reflect varying degrees of anomalous activities within the network. Key findings indicate that while the model exhibits potential in anomaly detection, as reflected by the progressive increase in triggered alerts and policy changes, its performance metrics, such as precision, recall, F1-score, and AUC, reveal limitations in its current application. The research contributes to the field by providing a detailed analysis of the Isolation Forest algorithm's performance in an SDN context and laying the groundwork for future enhancements in machine learning-based security measures within these networks.

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“…How do ML techniques improve the performance of Flying Ad Hoc Network? Since ML techniques have been broadly employed in wireless networks, especially in FANETs of autonomous UAVs to train network nodes to control, monitor, and predict different communication parameters, such as traffic patterns, node positioning, the behavior of wireless channels, and so forth (Guerber et al, 2021;Oliveira et al, 2021), an opportunity was identified to propose this SLR covering this subject domain. However, as this subject is complex, other side topics needed to be included to make the SLR more useful and comprehensive.…”

Section: Research Questions Raised In This Slrmentioning

confidence: 99%

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and form flying ad hoc networks (FANETs) to perform many different missions, ranging from delivery tasks to agriculture applications. Recently, FANETs have been integrated with different technologies, such as artificial intelligence (AI), virtual reality, and Internet of Things. Such new avenues for the use of UAVs directly impact the research on FANETs and cause some major challenges, such as security and physical layer issues, resource management, and UAV positioning issues that need to be addressed. Several researchers have been working for the last few years to propose AI and machine learning (ML)‐based solutions for different use cases in UAV‐based networks. They present the limitations of the existing research work and highlight some possible future works on FANETs. However, exhibiting the trends in the UAV research papers in a quantitative manner is still required to motivate researchers to rethink the research on FANETs. Therefore, this study covers more than 170 scientific publications extracted from five trusted academic databases published from 2013 to 2021 to provide a thorough overview of the main research and development statistics in the area of FANETs, the open challenges existing in this area and the ML‐based solutions to solve these challenges. In addition, the investigation of emerging technologies integrated with FANETs, as well as the simulation tools employed for evaluating FANETs' performance are discussed. Moreover, the future research directions in the area of FANETs are considered within a prospective vision discussion.

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Machine Learning and Software Defined Network to secure communications in a swarm of drones

Cited by 25 publications

References 21 publications

A Review on Machine Learning Techniques Used in VANET and FANET Networks

A Review on Machine Learning Techniques Used in VANET and FANET Networks

Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

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