Yıldız, Beytullah

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https://hdl.handle.net/20.500.14411/6931
Name Variants
Yıldız, Beytullah
B.,Yildiz
Yildiz, B
B., Yildiz
B., Yıldız
Beytullah, Yildiz
Y.,Beytullah
Yildiz,B.
Y., Beytullah
Yıldız,B.
Beytullah, Yıldız
Yildiz, Beytullah
B.,Yıldız
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Doçent Doktor
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beytullah.yildiz@atilim.edu.tr
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Software Engineering
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14632851900
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DZN-7662-2022

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Concurrency and Computation: Practice and Experience4
IEEE Access1
International Conference on Computational Science and Computational Intelligence (CSCI) -- DEC 13-15, 2023 -- Las Vegas, NV1
International Journal on Artificial Intelligence Tools1
Lecture Notes in Networks and Systems -- International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022 -- 16 September 2022 through 17 September 2022 -- Kocaeli -- 2919291
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Subject: Deep Q-learning

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    Citation - Scopus: 2
    Reinforcement Learning for Intrusion Detection
    (Springer Science and Business Media Deutschland GmbH, 2023) Saad,A.M.S.E.; Yildiz,B.
    Network-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 security concerns surrounding network-based technologies. Developing a robust intrusion detection system is crucial to solving the intrusion problem and ensuring the secure delivery of network-based technologies and services. In this paper, we propose a novel approach 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. The NSL-KDD dataset is used to create the reinforcement learning environment to train and evaluate the proposed model. The experimental results show that our proposed reinforcement learning approach outperforms other related solutions in the literature, achieving an accuracy that exceeds 93%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.