Document Details
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Document Type |
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Thesis |
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Document Title |
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DESIGNING A MITIGATION SYSTEM TO DETECT DISTRIBUTED DENIAL OF SERVICE (DDOS) IN IOT USING FOG COMPUTING تصميم نظام تخفيف هجمات DDoS في انترنت الأشياء بإستخدام الحوسبة الضبابية |
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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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Botnet attacks, such as DDoS, are among the most common attacks in IoT networks. A botnet is a collection of cooperated computing machines or IoT gadgets that criminal users manage remotely. Several strategies have been developed to reduce anomalies in IoT networks, such as DDoS. To increase the accuracy of the anomaly mitigation system and lower the false positive rate (FPR), some schemes use statistical or machine learning methodologies in the anomaly-based intrusion detection system (IDS) to mitigate an attack. Despite the proposed anomaly mitigation techniques, the mitigation of DDoS attacks in IoT networks remains a concern. Most anomaly mitigation methods fail because of the similarity between DDoS and normal network flows, leading to problems such as a high FPR, low accuracy, and a low detection rate. Furthermore, the limited resources in IoT devices make it difficult to implement anomaly mitigation techniques. In this thesis, an efficient anomaly mitigation system has been developed for the IoT network through the design and implementation of a DDoS attack detection system that uses a statistical method that combines three algorithms: exponentially weighted moving average (EWMA), K-nearest neighbors (KNN), and the cumulative sum algorithm (CUSUM). The integration of fog computing with the IoT has created a practical framework for implementing an anomaly mitigation strategy to address security issues such as botnet threats. The proposed module was evaluated using the Bot-IoT dataset. From the results, we conclude that our model has achieved a high accuracy (99.00\%) with a low false positive rate (FPR). We have also achieved good results in distinguishing between IoT and non-IoT devices. The research findings help the networking team better understand the distinction between IoT and non-IoT network traffic, allowing them to create higher-quality network policies regarding security, routing, and resource allocation.
Keywords: Internet of Things (IoT), Fog Computing, Intrusion Detection System (IDS), Distributed Denial-of-Service (DDoS), Cybersecurity. |
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Supervisor |
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Dr. Ahmed Alzahrani |
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Thesis Type |
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Doctorate Thesis |
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Publishing Year |
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1445 AH
2023 AD |
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Added Date |
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Tuesday, November 28, 2023 |
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Researchers
| رامي جمعان الزهراني | Alzahrani, Rami Jamaan | Researcher | Doctorate | |
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