Browsing by Author "Zaim, Abdul Halim"

Now showing 1 - 5 of 5

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Comparative Evaluation of Different Classification Techniques for Masquerade Attack Detection
(Inderscience Enterprises Ltd., 2020) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul Halim
Masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial basis for computer security. Although of considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low degree of false alarm rate is still a big challenge. In this paper, we present an extensive empirical study in the area of user behaviour profiling-based masquerade detection using six of different existed machine learning methods in Azure Machine Learning (AML) studio. In order to surpass previous studies on this subject, we used four free and publicly available datasets with seven data configurations are implemented from them. Moreover, eight well-known masquerade detection evaluation metrics are used to assess methods performance against each data configuration. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper.
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Cyberbullying Detection Through Deep Learning: A Case Study of Turkish Celebrities on Twitter
(IOS Press, 2023) Karadağ, Bulut; AKBULUT, AKHAN; Zaim, Abdul Halim
One of the ways that celebs maintain their fame in the modern era is by posting updates and photos to social media platforms like Twitter, Instagram, and Facebook. Comments left on their posts, however, expose them to cyberbullying. Cyberbullying, as a form of electronic device-based harassment, negatively impacts the lives of individuals. Thirty famous people from the fields of acting, art, music, politics, sports, and writing were chosen for this research. These notable figures include the top five Twitter followers of Turkey in each demographic. Between December 2019 and December 2020, comment responses for each celebrity were collated. Using the Deep Learning model, we were able to detect abuse content with an accuracy of 89%. Additionally, the percentage of celebrities exposed to cyberbullying by group was presented.
Open Access
A Design of an Integrated Cloud-Based Intrusion Detection System with Third Party Cloud Service
(Walter de Gruyter GmbH, 2021) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul Halim
Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature. © 2021 Wisam Elmasry et al., published by De Gruyter 2021.
Open Access
Identification of Phantom Movements With an Ensemble Learning Approach
(Pergamon-Elsevier Science Ltd., 2022) AKBULUT, AKHAN; Güngör, Feray; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdul Halim; Çatal, Çağatay
Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.
Open Access
Stacking-Based Ensemble Learning for Remaining Useful Life Estimation
(Springer, 2023) Türe, Begüm Ay; AKBULUT, AKHAN; Zaim, Abdul Halim; Çatal, Çağatay
Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA's turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.