Using Machine Learning to Secure IOT Systems
| dc.contributor.author | Mahmood, M.T. | |
| dc.contributor.author | Ahmed, S.R.A. | |
| dc.contributor.author | Ahmed, M.R.A. | |
| dc.date.accessioned | 2024-09-29T16:20:49Z | |
| dc.date.available | 2024-09-29T16:20:49Z | |
| dc.date.issued | 2020 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description | 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- Istanbul -- 165025 | en_US |
| dc.description.abstract | In this paper we will first find out the issues that are arises when we implement IOT systems and later we will fix these issues using Machine Learning techniques. we will implement an RFID (radio frequency identification) system which is seen as the prerequisite for the IOT and the research will also show a number of different technologies available when implementing such a system, showing their differences and why certain ones can be chosen over others for certain functional or security requirements. As stated the prototype IoT system will serves as a running example. The system implemented serves as a way for passengers at an airport to track their baggage after checking it in. The findings of implementing this system, combined with a literature study led us to find five main differences between IoT and traditional systems. Briefly summarized these differences are the following:1. Technical limitations of IoT devices.2. Physical environment plays a larger role in IoT systems. Many components of an IoT system will not be in a controlled environment.3. Lack of security-focus during design and implementation process.4. IoT devices are an interesting target for attackers as tools for DDoS attacks.5. The use cases of IoT systems are more often privacy sensitive. For the training, testing and validation of KDD (Knowledge Discovery and Data Mining) Cup 1999 dataset which is an IoT and cybersecurity based dataset, a well-known MATLAB R2019a software was used for this purpose. Furthermore, this works shows that the accuracy of machine learning models can mitigated to some degree with artificial neural network technique and achieving the accuracy of up to 97.2% with execution time of 2.11s only. © 2020 IEEE. | en_US |
| dc.identifier.doi | 10.1109/ISMSIT50672.2020.9254304 | |
| dc.identifier.isbn | 978-172819090-7 | |
| dc.identifier.scopus | 2-s2.0-85097661795 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ISMSIT50672.2020.9254304 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/9359 | |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | ANN | en_US |
| dc.subject | Intrusion detection | en_US |
| dc.subject | IOT (internet of things) | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Security | en_US |
| dc.title | Using Machine Learning to Secure IOT Systems | en_US |
| dc.type | Conference Object | en_US |
