Document Details

Document Type : Thesis 
Document Title :
DEEPDCA: INTRUSION DETECTION OVER IOT BASED ON ARTIFICIAL IMMUNE SYSTEM AND DEEP LEARNING
DeepDCA: كشف التسلل على إنترنت الأشياء بإستخدام نظام المناعة الاصطناعي والتعلم العميق
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : As the Internet of Things (IoT) recently attains tremendous popularity, this promising technology leads to a variety of security challenges. The traditional solutions do not fit the new challenges brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provide an opportunity to improve IoT security. In this thesis, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts DCA and Self Normalizing Neural Network. The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS select the convenient set of features from the IoT-Bot dataset and to perform their signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, it capable of performing better classification tasks than SVM, NB and similar performance with ANN classifier. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection. 
Supervisor : Prof. Daniyal Alghazzawi 
Thesis Type : Master Thesis 
Publishing Year : 1441 AH
2020 AD 
Added Date : Monday, January 20, 2020 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
سحر أحمد الظاهريAldhaheri, Sahar AhmedResearcherMaster 

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