Document Details

Document Type : Thesis 
Document Title :
A MULTI-CLASS NEURAL NETWORK MODEL FOR RAPID DETECTION OF IOT BOTNET ATTACKS
نموذج الشبكة العصبية ذو التصنيف المتعدد للاكتشاف السريع لهجمات بوت نت إنترنت الأشياء
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The tremendous number of Internet of Things (IoT) devices and their widespread use have made our lives considerably more manageable. At the same time, however, , the vulnerability of these innovations means that our day-to-day existence is surrounded by insecure devices, thereby facilitating ways for cybercriminals to launch various attacks by large-scale robot networks (botnets) through IoT. This problem is further heightened by the constraints of the IoT on security techniques due to limited resources including central processing units (CPUs), memory, and power consumption. In consideration of these issues, we propose a lightweight neural network-based model to rapidly detect IoT botnet attacks. The model was developed using FastGRNN algorithm which is a lightweight and fast version of the recurrent neural network. In addition, it is independent and does not require any specific equipment or software to fetch the required features for learning and detection processes. Therefore, only packet headers are required to complete learning and detection. Furthermore, the model provides multi-classification, which is necessary for taking appropriate countermeasures to understand and stop the attacks. According to the conducted experiments, the proposed model is accurate and achieves 99.99%, 99.04% as F1 score for MedBIoT and Mirai-RGU datasets in addition, to fulfilling IoT constraints regarding complexity and speed. It is less complicated in terms of computations, and it provides real-time detection that outperformed the state-of-the-art, achieving a detection time ratio of 1:5 for MedBIoT dataset and a ratio of 1:8 for Mirai-RGU dataset. 
Supervisor : Dr. Maysoon Abulkhair 
Thesis Type : Master Thesis 
Publishing Year : 1442 AH
2020 AD 
Co-Supervisor : Dr. Entisar Alkayal 
Added Date : Friday, January 29, 2021 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
هيفاء محمد الزهرانيAlzahrani, Haifaa MohammedResearcherMaster 

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