Document Details

Document Type : Thesis 
Document Title :
Intrusion detection in IoT using deep learning.
كشف التسلل في إنترنت الأشياء باستخدام التعلم العميق
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to innovative developments that continue to face challenges in solving problems related to security methods for IoT devices. Effective security methods, such as deep learning for intrusion detection, have been introduced. Recent research has focused on improving deep learning algorithms for improved security in IoT. This research explores intrusion detection methods implemented using deep learning, compares the performance of different deep learning methods, and identifies the best method for implementing intrusion detection in IoT. This research is conducted using deep learning models based on convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). Four datasets, namely, Bot-IoT, CICIDS 2017, UNSW-NB15, and IoT-23, are compared to identify the boundaries of each dataset. Three classifiers are used to determine the accuracy, precision, and F1 scores of each dataset. The research results reveal the suitable datasets for improving the detection of malware, data breaches, and abnormal network activities in IoT. For all the datasets, the LSTM model is the most accurate, followed by GRU and CNN. The research is expected to help improve security in IoT devices and assist researchers in identifying the best method for implementing intrusion detection methods in IoT networks. Key Word: Deep Learning; Intrusion Detection; IoT; Convolutional Neural Networks; Long Short-term Memory; Gated Recurrent Units; Cybersecurity; Bot-IoT, CICIDS 2017, UNSW-NB15, IoT-23. 
Supervisor : Dr. Iftikhar Ahmad Khan 
Thesis Type : Master Thesis 
Publishing Year : 1444 AH
2023 AD 
Added Date : Sunday, July 2, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
علاء محمد باناعمهBanaamah, Alaa MohammedResearcherMaster 

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