Browsing by Author "Dag,H."

Now showing 1 - 7 of 7

Citation Count: 0
Enhancing Malware Classification: A Comparative Study of Feature Selection Models with Parameter Optimization
(Institute of Electrical and Electronics Engineers Inc., 2024) Dağ, Hasan; Dag,H.
This study assesses the impact of seven feature selection algorithms (Minimum Redundancy Maximum Relevance (MRMR), Mutual Information (MI), Chi-Square (Chi), Leave One Feature Out (LOFO), Feature Relevance-based Unsupervised Feature Selection (FRUFS), A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization (AGRM), and BoostARoota) across two malware datasets (Microsoft and API call sequences) using three machine learning models (Extreme Gradient Boosting (Xgboost), Random Forest, and Histogram-Based Gradient Boosting (Hist Gradient Boosting)). The analysis reveals that no feature selection algorithm uniformly outperforms the others as their effectiveness varies based on the dataset and model characteristics. Specifically, BoostARoota demonstrated significant compatibility with the Microsoft dataset, especially after parameter optimization, whereas its performance varied with the API call sequences dataset, suggesting the need for customized parameter selection. This study highlights the necessity of tailored feature selection approaches and parameter adjustments to optimize machine learning model performance across different datasets. © 2024 IEEE.
Citation Count: 0
Garbage in, Garbage Out: A Case Study on Defective Product Prediction in Manufacturing
(Institute of Electrical and Electronics Engineers Inc., 2023) Dağ, Hasan; Ucar,B.E.; Saygut,I.; Duzgun,B.; Demirkiran,F.; Dag,H.
Despite their potential business value and invest-ments, data science projects often fail owing to a lack of preparedness, implementation challenges, and poor data quality. This study aimed to develop a machine learning model for predicting defective products in the dyeing process within the manufacturing domain. However, inadequate importance given to data by the involved factory, insufficient data quality, and the lack of the necessary technical infrastructure for data science projects have hindered attaining desired results. This study emphasizes to academic researchers and industry experts the significance of data quality and technical infrastructure, highlights how these deficiencies can impact the success of a data science project, and provides several recommendations. © 2023 IEEE.
Citation Count: 0
The Open Source Intelligence (OSINT) in the Electricity Sector: Balancing Utility and Responsibility
(Society for Imaging Science and Technology, 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag,H.; Creutzburg,R.
Critical infrastructure is the backbone of modern societies, and protecting this infrastructure is essential to ensure the stability of societies and economies. The electricity sector is one of the most critical infrastructures, and any disruption can have significant consequences. The threat landscape in this sector is constantly evolving. With the increasing sophistication of cyber-attacks and other threats, it has become essential to use innovative technologies to identify and mitigate them. Open Source Intelligence (OSINT) technologies have emerged and offer valuable tools for identifying and mitigating these threats. This article presents an in-depth overview of OSINT technologies and their applications in the protection of critical infrastructure, with an emphasis on the electricity sector. It discusses the vulnerabilities of the electricity sector, the types of OSINT technologies, and the benefits they provide. Case studies of successful applications of OSINT technologies in the electricity sector are presented to illustrate their effectiveness. This article also examines organizations’ challenges in implementing OSINT technologies, including technological, legal, and financial challenges. Finally, the article concludes by offering recommendations for successfully implementing OSINT technologies to protect critical infrastructure, particularly in the electricity sector. The insights offered in this article will be helpful for policymakers, security professionals, and anyone interested in protecting critical infrastructure. © 2024, Society for Imaging Science and Technology.
Citation Count: 0
Optimizing Collective Building Management through a Machine Learning-based Decision Support System
(Institute of Electrical and Electronics Engineers Inc., 2023) Dağ, Hasan; Kiran,H.; Dogan,E.; Dag,H.; Ozyuruyen,B.; Cakar,T.
This study presents the design, implementation, and evaluation of a Decision Support System (DSS) developed for Collective Building Management. Given the potential advantages of machine learning techniques in this domain, the research explores how these techniques can be used to improve collective building management. The dataset consists of 824,932 records and 15 attributes, after preprocessing the data to fill in missing values with the median. The random forest algorithm was chosen for model training and achieved a performance rate of 71.2%. This model can be used to optimize decision processes in collective building management. The proposed prototype is notable for its ability to automatically generate operational plans. In conclusion, machine learning-based DSSs are effective tools for collective building management. © 2023 IEEE.
Citation Count: 0
Power consumption estimation using in-memory database computation
(Institute of Electrical and Electronics Engineers Inc., 2016) Dağ, Hasan; Alamin,M.
In order to efficiently predict electricity consumption, we need to improve both the speed and the reliability of computational environment. Concerning the speed, we use in-memory database, which is taught to be the best solution that allows manipulating data many times faster than the hard disk. © 2016 IEEE.
Securereg: Combining Nlp and Mlp for Enhanced Detection of Malicious Domain Name Registrations
(Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag,H.; Creutzburg,R.; 0
The escalating landscape of cyber threats, charac-terized by the registration of thousands of new domains daily for lar ge-scale Inter net attacks such as spam, phishing, and drive-by downloads, underscor es the imperati ve for innovative detection methodologies. This paper introduces a cutting-edge approach for identifying suspicious domains at the onset of the registration process. The accompanying data pipeline generates crucial featur es by comparing new domains to register ed do-mains, emphasizing the crucial similarity score. The proposed system analyzes semantic and numerical attrib utes by leveraging a novel combination of Natural Language Processing (NLP) techniques, including a pretrained CANINE model and Multilayer Perceptr on (MLP) models, providing a robust solution for early threat detection. This integrated Pretrained NLP (CANINE) + MLP model showcases the outstanding perf ormance, surpassing both individual pretrained NLP models and standalone MLP models. With an PI score of 84.86% and an accuracy of 84.95%on the SecureReg dataset, it effecti vely detects malicious domain registrations. The finding demonstrate the effecti veness of the integrated appr oach and contrib ute to the ongoing efforts to develop proactive strategies to mitigate the risks associated with illicit online activities through the ear ly identificatio of suspicious domain registrations. © 2024 IEEE.
Citation Count: 0
Towards Better Cyber Security Consciousness: The Ease and Danger of OSINT Tools in Exposing Critical Infrastructure Vulnerabilities
(Institute of Electrical and Electronics Engineers Inc., 2023) Ecevit, Mert İlhan; Dağ, Hasan; Naqvi,N.Z.; Creutzburg,R.; Dag,H.
This article explores open-source intelligence (OS-INT) to identify the vulnerabilities and loopholes in power grid systems, focusing on an electrical distribution company operating in Turkey. The study emphasizes the potential risks of sharing publicly available information on social media accounts, websites, reports, and press releases which most companies overlook. It highlights that individuals or adversaries can exploit this information to harm companies and countries that may not be fully aware of these vulnerabilities. OSINT tools can efficiently gather interpretable data on a company, which companies unknowingly share. By refining the collected data, the study aims to understand the technologies used, their software versions, and any associated vulnerabilities. Web scraping tools extract data from the company's website, which may contain critical information about updates, ongoing systems, and technologies. The article provides a comprehensive understanding of the potential risks and vulnerabilities associated with sharing sensitive information and the various OSINT tools and techniques that can be used to identify and address these vulnerabilities. The importance of vigilance against the potential harm that remote or unrelated individuals can inflict using OSINT capabilitiesis underscored. This study shows how easy it is to detect vulnerabilities in a critical infrastructure system using OSINT tools. © 2023 IEEE.