Internet of thing (IoT) cyber security with machine learning
| dc.contributor.advisor | Uçan, Osman Nuri | |
| dc.contributor.author | Khaleel, Arshad Mohammed Khaleel | |
| dc.date.accessioned | 2022-12-05T06:09:47Z | |
| dc.date.available | 2022-12-05T06:09:47Z | |
| dc.date.issued | 2022 | en_US |
| dc.date.submitted | 2022 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | Since the invention of the Internet of Things, the number of connected devices has grown exponentially (IoT). For network operators, IoT service providers, and end-users, this expansion has created more security issues. Security standards for IoT devices are difficult to implement because they are so diverse and constrained. Massive Distributed Denial of Service attacks has been carried out using Internet of Things devices. A security awareness campaign in the core network is the only practical way to protect services from unsecured IoT devices that access mobile core networks. In the Internet of Things (IoT), there are a group of connected devices or machines that can transfer data across a network without the intervention of a human. Multihop wireless connections between sensors embedded in computer objects and a local network or the internet are used in the technology. IoT devices have become increasingly popular as a result of the rapid advancements in technology around the world. To be sure, IoT producers have influenced its price and availability, but the system itself provides only rudimentary safeguards and is largely untested due to the wide variety of applications it can be used for. Botnet detection has thus increased in importance as a component of network security. To detect botnet attacks, we employ a variety of AI calculations in this paper, and then use the most accurate and precise calculation to look at the subsidiaries. | en_US |
| dc.identifier.citation | Khaleel, Arshad Mohammed Khaleel. (2022). Internet of thing (IOT) cyber security with machine learning. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/3066 | |
| dc.identifier.yoktezid | 746267 | |
| dc.institutionauthor | Khaleel, Arshad Mohammed Khaleel | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | IoT Security | en_US |
| dc.subject | Botnet | en_US |
| dc.subject | DDoS | en_US |
| dc.subject | Internet of Things | en_US |
| dc.subject | Machine Learning Classifier | en_US |
| dc.title | Internet of thing (IoT) cyber security with machine learning | |
| dc.type | Master Thesis |
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