Browsing by Author "Saad, Ahmed Mohamed Saad Emam"
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Item Reinforcement Learning for Intrusion Detection(2022-01-17) Saad, Ahmed Mohamed Saad Emam; Yıldız, BeytullahNetwork-based technologies such as cloud computing, web services, and Internet of Things systems are becoming widely used due to their flexibility and preeminence. On the other hand, the exponential proliferation of network-based technologies exacerbated network security concerns. Intrusion takes an important share in the secu rity concerns surrounding network-based technologies. Developing a robust intrusion detection system is crucial to solve the intrusion problem and ensure the secure delivery of network-based technologies and services. In this thesis, a novel approach was proposed using deep reinforcement learning to detect intrusions to make network applications more secure, reliable, and efficient. As for the reinforcement learning approach, Deep Q-Learning is used alongside a custom-built Gym environment that mimics network attacks and guides the learning process. A supervised deep learning solution using a Long-Short Term Memory architecture is implemented to serve as a baseline. The NSL-KDD dataset is used to create the reinforcement learning environment and to train and evaluate the baseline model. The performance results of the proposed reinforcement learning approach show great superiority over the baseline model and the other relevant solutions from the literature.