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Author: Peker, SerhatSubject: Machine learning

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    A Comparison of Neural Network Approaches for Network Intrusion Detection
    (Springer international Publishing Ag, 2020) Oney, Mehmet Ugur; Peker, Serhat
    Nowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.
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    Article
    Citation - WoS: 12
    Citation - Scopus: 20
    A Hybrid Approach for Predicting Customers' Individual Purchase Behavior
    (Emerald Group Publishing Ltd, 2017) Peker, Serhat; Kocyigit, Altan; Eren, P. Erhan
    Purpose - Predicting customers' purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers' individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them. Design/methodology/approach - The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings - A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior. Practical implications - The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies. Originality/value - This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers' individual purchase behaviors.