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Document Type |
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Thesis |
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Document Title |
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IOT CYBER ATTACKS DETECTION USING MACHINE LEARNING الكشف عن هجمات إنترنت الأشياء باستخدام التعلم الآلي |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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IoT Cyber Attacks Detection Using Machine Learning By: Jadel Marzouq Alsamiri Supervised By Dr. Khalid Ateatallah Alsubhi ABSTRACT The Internet of Things (IoT) combines hundreds of millions of devices which are capable of interact with each other with minimum human interaction. IoT is one of the fastest- growing areas in of computing; however, the reality is that in the extremely hostile environment of the internet, IoT is vulnerable to numerous types of cyberattacks. To resolve this, practical countermeasures need to be established to secure IoT networks, such as network anomaly detection. Regardless that attacks cannot be wholly avoided forever, early detection of an attack is crucial for practical defense. Since IoT devices have low storage capacity and low processing power, traditional high-end security solutions to protect an IoT system are not appropriate. Also, IoT devices are now connected without human intervention for longer periods. This implies that intelligent network-based security solutions like machine learning solutions must be developed. Although many studies in recent years have discussed the use of Machine Learning (ML) solutions in attack detection problems, little attention has been given to the detection of attacks specifically in IoT networks. In this study, we aim to contribute to the literature by evaluating various machine learning algorithms that can be used to quickly and effectively detect IoT network attacks. A new dataset, Bot-IoT, is used to evaluate various detection algorithms. In the implementation phase, seven different machine learning algorithms were used, and most of them achieved high performance. New features were extracted from the Bot-IoT dataset during the implementation and compared with studies from the literature, and the new features gave better results. |
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Supervisor |
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Dr. Khalid Ateatallah Alsubhi |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1441 AH
2020 AD |
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Added Date |
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Monday, February 3, 2020 |
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Researchers
| جادل مرزوق السميري | Alsamiri, Jadel Marzouq | Researcher | Master | |
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