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    Cloud computing and 5G challenges and open issues
    (Intelektual Pustaka Media Utama, 2022) Ullah, Arif; Aznaoui, Hanane; Şahin, Canan Batur; Sadie, Mahanz; Dinler, Ozlem Batur; Imane, Laassar
    The obtainable fourth-generation technology (4G) networks have been extensively used in the cloud application and are constantly evolving to match the needs of the future cloud applications. The fifth-generation (5G) networks are probable to immense expand today's cloud that can boost communication operations, cloud security, and network challenges and drive the cloud future to the edge and internet of things (IoT) applications. The existing cloud solutions are facing a number of challenges such as large number of connection of nodes, security, and new standards. This paper reviews the current research state-of-the-art of 5G cloud, key-enabling technologies, and current research trends and challenges in 5G along with cloud application. © 2022, Intelektual Pustaka Media Utama. All rights reserved.
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    Detection of cyber attacks in electric vehicle charging systems using a remaining useful life generative adversarial network
    (Nature Portfolio, 2025) Tanyildiz, Hayriye; Sahin, Canan Batur; Dinler, Ozlem Batur; Migdady, Hazem; Saleem, Kashif; Smerat, Aseel; Abualigah, Laith
    Cybersecurity attacks targeting electric vehicle supply equipment (EVSE) and the broader electric vehicle (EV) ecosystem have become an escalating concern with the increasing adoption of EVs and the growing connectivity of the infrastructure supporting them. The present research aims to contribute to continuing cybersecurity studies on electric vehicle charging stations. In line with this objective, this study proposes the remaining useful life (RUL) approach to demonstrate the potential impact of estimating the remaining time of a cyber attack on EVSE and what revolutionary changes it can bring to cyber security strategies using a generative adversarial network (GAN). By taking a proactive stance, the manuscript will increase security and reduce the economic and reputational losses associated with cyber incidents. Accurate RUL estimates present valuable information about the status of the EVSE infrastructure. Thus, informed decisions on maintenance and crew scheduling are taken. To test the technique's effectiveness, we assess this approach on attack scenarios, including network and host attacks on the EV charger (Electric Vehicle Supply Equipment-EVSE) in idle and charging states. Furthermore, we assess the prediction results of different deep learning models, such as gated recurrent units (GRUs), long short-term memory (LSTM), recurrent neural networks (RNNs), convolution neural networks (CNNs), multi-layer perceptron (MLP), and dense layer integrated with generative adversarial networks (GANs), using mean absolute error (MAE), root mean square error (RMSE), mean squared error (MSE), and R-squared (R2). Afterward, we compare the error measurements with models, such as hybrid GAN-LSTM, GAN-GRU, GAN-RNN, GAN-CNN, GAN-MLP, and GAN-Dense Layer. The GAN-GRU model exhibits the highest accuracy with the lowest MAE (0.0281). On the contrary, the GAN-CNN model displays the best overall performance concerning error consistency and variance explained. According to the results, integrating GAN into these architectures improves predictive accuracy and the model's ability to identify potential attacks in advance and decreases error rates.
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    Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems
    (Mdpi, 2024) Tanyildiz, Hayriye; Sahin, Canan Batur; Dinler, Ozlem Batur
    Industrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities. With increased internet connectivity and integration with the Internet of Things (IoT), ICSs become more vulnerable to cyberattacks, which can have serious consequences, such as service interruption, financial losses, and security hazards. Threat actors target these systems with sophisticated attacks that can cause devastating damage. Cybersecurity vulnerabilities in ICSs have recently led to increasing cyberattacks and malware exploits. Hence, this paper proposes to develop a security solution with dynamic and adaptive deceptive patching strategies based on studies on the use of deceptive patches against attackers in industrial control systems. Within the present study's scope, brief information on the adversarial training method and window size manipulation will be presented. It will emphasize how these methods can be integrated into industrial control systems and how they can increase cybersecurity by combining them with deceptive patch solutions. The discussed techniques represent an approach to improving the network and system security by making it more challenging for attackers to predict their targets and attack methods. The acquired results demonstrate that the suggested hybrid method improves the application of deception to software patching prediction, reflecting enhanced patch security.