Browsing by Author "Koca, Murat"

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A Comprehensive Bibliometric Analysis of Big Data and Cyber Security: Intellectual Structure, Trends, and Global Collaborations
(Springer London Ltd, 2025) Koca, Murat; Ciftci, Sabahattin
The rapid growth of the big data ecosystem and the increasing complexity of cyber threats need a thorough examination of research at the intersection of these two domains. This study conducts a multidimensional bibliometric analysis within the framework of the keywords "Big Data" and "Cybersecurity" based on 3354 publications published in the Web of Science Core Collection database between 1975 and 2025. Structures such as document kinds, trends in publication and citation, most influential studies, clusters of keywords, collaboration networks, country and institution contributions, and discipline distribution were all thoroughly examined as part of the investigation. The results indicate that conference proceedings constitute the predominant document type, with China and the USA leading in publication and impact. Authors like Kim-Kwang Raymond Choo exhibit significant influence, and collaboration networks are predominantly clustered in Asia and North America. In addition, conceptual clusters obtained from abstracts define the Big Data-Cybersecurity relationship in terms of data privacy, intrusion detection, and IoT integration. This study both makes structural gaps in the literature visible and provides guiding insights for researchers, practitioners, and policymakers.
Enhancing Network Security: A Comprehensive Analysis of Intrusion Detection Systems
(2024) Koca, Murat; Avcı, Dr. İsa
Siber saldırılarının artan karmaşıklığı ve ilerlemesi göz önüne alındığında, etkili saldırı tespit sistemlerinin varlığı ağ güvenliğinin önemli bir bileşeni haline gelmiştir. Makine öğrenimi yöntemleri, bu tür saldırıları belirlemek ve azaltmak için potansiyel bir strateji haline gelmiştir. Bu makale, makine öğrenimi tekniklerini kullanarak saldırı tespitinin kapsamlı bir incelemesini gerçekleştirmiştir. Amaç, mevcut araştırma durumunun kapsamlı bir analizini sunmak, engelleri belirlemek ve bu alandaki olası çözümleri vurgulamaktır. Makale, saldırı tespitinin önemini ve geleneksel kural tabanlı sistemlerin kısıtlamalarını inceleyerek başlamaktadır. Ardından, makine öğreniminin temel fikirleri ve kavramları ile saldırı tespiti alanındaki pratik uygulamalarına derinlemesine inmektedir. Bu çalışmada, karar ağaçları, sinir ağları, destek vektör makineleri ve topluluk yöntemleri dahil olmak üzere çeşitli makine öğrenimi algoritmalarının kapsamlı bir incelemesi sunulmaktadır. Bu çalışmanın temel amacı, farklı saldırı türlerini tespit etmek için bu yöntemleri kullanmanın etkinliğini ve kısıtlamalarını incelemektir. NSL-KDD veri setini sınıflandırmak için üç algoritma kullanılmıştır: Basamaklı Geri Yayılımlı Sinir Ağları (CBPNN), Katmanlı Tekrarlayan Sinir Ağı (LRNN) ve İleri-Geri Yayılımlı Sinir Ağları (FBPNN). Yapılan çalışma sonucunda, CBPNN'nin %95 doğruluk elde ederek daha iyi performans gösterdiğini göstermiştir.
Intelligent Transportation System Technologies, Challenges and Security
(Mdpi, 2024) Avci, Isa; Koca, Murat
Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.
A Novel Hybrid Model Detection of Security Vulnerabilities in Industrial Control Systems and Iot Using Gcn Plus Lstm
(Ieee-inst Electrical Electronics Engineers inc, 2024) Koca, Murat; Avci, Isa
In this study, we address critical security vulnerabilities in Industrial Control Systems (ICS) and the Internet of Things (IoT) by focusing on enhancing collaboration and communication among interconnected devices. Recognizing the inherent risks and the sophisticated nature of cyber threats in such environments, we introduce a novel and complex implementation that leverages the synergistic potential of Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. This approach is designed to intelligently predict and detect intrusion attempts by analyzing the dynamic interactions and data flow within networked systems. Our methodology not only differentiates between the operational nuances of various IoT routing mechanisms but also tackles the core design challenges faced by ICS. Through rigorous experimentation, including the deployment of our model in simulated high-risk scenarios, we have demonstrated its efficacy in identifying and mitigating deceptive connectivity disruptions with a remarkable accuracy rate of 99.99%. This performance underscores the models capability to serve as a robust security layer, ensuring the integrity and resilience of ICS networks against sophisticated cyber threats. Our findings contribute a significant advancement in the field of cybersecurity for ICS and IoT, proposing a comprehensive framework that can be centrally integrated with existing security information and incident management systems for enhanced protective measures.
A Novel Security Risk Analysis Using the Ahp Method in Smart Railway Systems
(Mdpi, 2024) Avci, Isa; Koca, Murat
Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will cause a damaged corporate reputation and revenue losses. In this study, cybersecurity attack methods in smart rail systems were determined, and cyber events occurring worldwide through these technologies were analyzed. Risk analysis in terms of transportation safety in smart rail systems was determined by considering the opinions of 10 different experts along with the Analytic Hierarchical Process (AHP) performance criteria. Informatics experts were selected from a group of people with at least 5-15 years of experience. According to these risk analysis calculations, cybersecurity stood out as the most critical security risk at 27.74%. Other risky areas included physical security, calculated at 14.59%, operator errors at 16.20%, and environmental security at 10.93%.
Predicting Ddos Attacks Using Machine Learning Algorithms in Building Management Systems
(Mdpi, 2023) Avci, Isa; Koca, Murat
The rapid growth of the Internet of Things (IoT) in smart buildings necessitates the continuous evaluation of potential threats and their implications. Conventional methods are increasingly inadequate in measuring risk and mitigating associated hazards, necessitating the development of innovative approaches. Cybersecurity systems for IoT are critical not only in Building Management System (BMS) applications but also in various aspects of daily life. Distributed Denial of Service (DDoS) attacks targeting core BMS software, particularly those launched by botnets, pose significant risks to assets and safety. In this paper, we propose a novel algorithm that combines the power of the Slime Mould Optimization Algorithm (SMOA) for feature selection with an Artificial Neural Network (ANN) predictor and the Support Vector Machine (SVM) algorithm. Our enhanced algorithm achieves an outstanding accuracy of 97.44% in estimating DDoS attack risk factors in the context of BMS. Additionally, it showcases a remarkable 99.19% accuracy in predicting DDoS attacks, effectively preventing system disruptions, and managing cyber threats. To further validate our work, we perform a comparative analysis using the K-Nearest Neighbor Classifier (KNN), which yields an accuracy rate of 96.46%. Our model is trained on the Canadian Institute for Cybersecurity (CIC) IoT Dataset 2022, enabling behavioral analysis and vulnerability testing on diverse IoT devices utilizing various protocols, such as IEEE 802.11, Zigbee-based, and Z-Wave.
Real-Time Security Risk Assessment From Cctv Using Hand Gesture Recognition
(Ieee-inst Electrical Electronics Engineers inc, 2024) Koca, Murat
Closed-Circuit Television (CCTV) surveillance systems, long associated with physical security, are becoming more crucial when combined with cybersecurity measures. Combining traditional surveillance with cyber defenses is a flexible method for protecting against both physical and digital dangers. This study introduces the use of convolutional neural networks (CNNs) and hand gesture detection using CCTV data to perform real-time security risk assessments. The suggested method's emphasis on automated extraction of key information, such as identity and behavior, illustrates its special use in silent or acoustically challenging settings. This study uses deep learning techniques to develop a novel approach for detecting hand gestures in CCTV images by automatically extracting relevant features using a media-pipe architecture. For instance, it facilitates risk assessment through the use of hand gestures in noisy environments or muted audio streams. Given this method's uniqueness and efficiency, the suggested solution will be able to alert appropriate authorities in the event of a security breach. There seems to be considerable opportunity for the development of applications in several domains of security, law enforcement, and public safety, including but not limited to shopping malls, educational institutions, transportation, the armed forces, theft, abduction, etc.