Browsing by Author "Saad,A.M.S.E."

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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.
Citation - Scopus: 1
A Systematical Review Study To Investigate the Use of Biometric Security Techniques in Automatic Teller Machines: Insight From the Past 15 Years
(Institute of Electrical and Electronics Engineers Inc., 2019) Saad,A.M.S.E.
In a Technological financial world where everything is progressing very fast and the amount of financial transactions through the automatic teller machines (ATM) on a daily basis is increasing. In parallel to these developments, fraudulent attacks and identity theft targeting to access the ATM and steal money or use bank accounts. Those attacks are always possible through the weak security points in the ATMs. The main aim of this study is to investigate and categorize the different approaches taken to overcome those weak security points by using biometric data along with some traditional methods. Accordingly, a systematical mapping was conducted by focusing on research studies published in the years 2004 to 2019 and addressing to the ATMs and biometric security technologies. After an intense investigation, 23 different systems were investigated using different single and multi-biometric models which make use of 8 different biometric technologies and 5 different traditional technologies as key components. As a conclusion from this research that researchers are leaning towards using biometric data in enhancing the security of the ATMs especially in the past 4 years more than they did before. © 2019 IEEE.