Document Details

Document Type : Thesis 
Document Title :
Predicting Malicious Software in IoT Environment: Data Mining Approach
التنبؤ بالبرمجيات الضارة في بيئة إنترنت الأشياء: نهج التنقيب عن البيانات
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The Internet of Things (IoT) enables devices to sense and respond by using the power of computing to autonomously come up with the best solutions for any industry today. However, IoT has vulnerabilities that make it easily hacked by cybercriminals. Cybercriminals know where IoT vulnerabilities are, such as unsecured update mechanisms and malicious software (malware) to attack IoT devices. The recently posted IoT-23 dataset based on several IoT devices such as Echo devices, Hue device and Somfy door lock device were used for machine learning classification algorithms and data mining techniques with training and testing for predictive modelling of a variety of malware attacks like Distributed Denial of Service (DDoS), Command and Control (C&C) and various IoT botnet like Mirai and Okiru. This research aims to develop predictive modeling that will predict malicious software in order to protect IoT environment and reduce vulnerabilities by using machine learning and data mining techniques. We collected, analyzed, and processed benign and several malware in IoT network traffic. Malware prediction is crucial in maintaining the safety of IoT devices from cybercriminals. Furthermore, Principal Component Analysis (PCA) method was applied to determine the important features of IoT-23 dataset. In addition, we have compared our study with previous studies that used the same dataset in terms of accuracy rate and other performance metrics. The results show that Random Forest classifier achieved a classification accuracy of 97.14% as well as predicted 8,754 samples various types of malware and obtained 96.44% of the Area Under Curve (AUC). Thus, Random Forest classifier outperforms several baseline machine learning classification models. 
Supervisor : Prof. Md Abdul Hamid 
Thesis Type : Master Thesis 
Publishing Year : 1444 AH
2023 AD 
Co-Supervisor : Dr. Husam Lahza 
Added Date : Tuesday, June 6, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
عبدالمحسن عطاالله الحربيAlharbi, Abdulmohsen AtallahResearcherMaster 

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