Artificial intelligence-enhanced intrusion detection systems for drone security: a real-time evaluation of algorithmic efficacy in mitigating wireless vulnerabilities

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dc.authoridhttps://orcid.org/0000-0001-8413-7570
dc.contributor.authorŞentürk, Kenan
dc.contributor.authorGörmüş, Ahmet Faruk
dc.contributor.authorGönen, Serkan
dc.contributor.authorBarışkan, Mehmet Ali
dc.contributor.authorDurmaz, Ahmet Kaan
dc.date.accessioned2025-06-18T08:02:55Z
dc.date.available2025-06-18T08:02:55Z
dc.date.issued2025
dc.departmentMühendislik ve Mimarlık Fakültesi
dc.description.abstractAdvancements in science and technology have provided extensive opportunities and conveniences for mankind. One prime example of these advancements is wireless communication technology. This technology provides users with mobility during communication, initiating a paradigm shift. The convenience of wireless communication technology has initiated the production of versatile devices. Among these technologies developed in recent years for observation and detection purposes in various fields, drones have taken a leading role. Drones, with their versatile applications and access to real-time data, are being used in various operations. With such utilization, humans are increasingly interacting with these systems, leading to natural human-drone interaction. However, in these human-drone interactions, as is the case with many wireless devices, security often becomes an afterthought, leaving many drones vulnerable to cyber attacks. The most effective way to protect against these attackers is to conduct vulnerability analyses of the systems we use against emerging threats and address the detected vulnerabilities. This paper investigates the vulnerabilities of wireless communication regarding remote connectivity usage of a commercial drone, the DJI Ryze Tello, with the aim of examining its weaknesses. In this context, a test environment was created to reveal problems and threats in drone technology through attacks executed on the test environment (DEAUTH ATTACK, Port Scan DOS, DDoS, and MitM). Following the identification of these vulnerabilities, an artificial intelligence-based study was carried out to detect these attacks. In the study, the percentages of attack detection using different algorithms were verified with graphs.
dc.identifier.doi10.1007/s10586-024-04911-8
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.issue3
dc.identifier.urihttps://hdl.handle.net/11363/9941
dc.identifier.volume28
dc.identifier.wos001401578800005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthoridhttps://orcid.org/0000-0001-8413-7570
dc.language.isoen
dc.publisherSPRINGER, ONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES
dc.relation.ispartofCLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDrones
dc.subjectDrone security
dc.subjectCyber security
dc.subjectArtificial intelligence
dc.subjectIoT
dc.titleArtificial intelligence-enhanced intrusion detection systems for drone security: a real-time evaluation of algorithmic efficacy in mitigating wireless vulnerabilities
dc.typeArticle

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