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Öğe A Hybrid Graph Neural Network Model for Predicting Cyber Attacks From Heterogeneous and Dynamic Network Data(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Soylu, Mucahit; Das, ResulWith valuable data constantly under attack, reactive security measures are no longer sufficient. Predicting cyber threats before they emerge is crucial. Cyberattacks do not occur randomly; they have a systematic underlying pattern. By discovering these patterns, it is possible to predict cyberattacks in advance. Unraveling the mysteries of these evolutionary patterns is quite challenging. Considering the potential of Graph Neural Networks to strengthen cybersecurity defenses, this paper proposes a new hybrid model, DyMHAG (Dynamic Meta-Path Heterogeneous Attention Graph). We propose a novel hybrid GNN model that integrates meta-path-based graph attention networks with the Gated Recurrent Unit (GRU) mechanism for temporal data processing. Our method consists of three layers: node-level attention-based graph embedding, meta-path-level attention-based graph embedding, and evolutionary pattern learning. This model aims to effectively capture complex relational structures and temporal dependencies in heterogeneous and temporally dynamic network data and provide a proactive solution for cyberthreat prediction. Preliminary evaluations indicate that our hybrid model not only improves prediction accuracy but also reduces the false positive rate, providing a more reliable defense against emerging cyber threats.Öğe A Novel Approach for Cyber Threat Analysis Systems Using BERT Model from Cyber Threat Intelligence Data(Mdpi, 2025) Demirol, Doygun; Das, Resul; Hanbay, DavutAs today's cybersecurity environment is becoming increasingly complex, it is crucial to analyse threats quickly and effectively. A delayed response or lack of foresight can lead to data loss, reputational damage, and operational disruptions. Therefore, developing methods that can rapidly extract valuable threat intelligence is a critical need to strengthen defence strategies and minimise potential damage. This paper presents an innovative approach that integrates knowledge graphs and a fine-tuned BERT-based model to analyse cyber threat intelligence (CTI) data. The proposed system extracts cyber entities such as threat actors, malware, campaigns, and targets from unstructured threat reports and establishes their relationships using an ontology-driven framework. A named entity recognition dataset was created and a BERT-based model was trained. To address the class imbalance, oversampling and a focal loss function were applied, achieving an F1 score of 96%. The extracted entities and relationships were visualised and analysed using knowledge graphs, enabling the advanced threat analysis and prediction of potential attack targets. This approach enhances cyber-attack prediction and prevention through knowledge graphs.Öğe A key review on graph data science: The power of graphs in scientific studies(Elsevier, 2023) Das, Resul; Soylu, MucahitThis comprehensive review provides an in-depth analysis of graph theory, various graph types, and the role of graph visualization in scientific studies. Graphs serve as powerful tools for modeling and analyzing complex systems in diverse disciplines. The introduction highlights the importance of graphs as a visual representation in scientific research, enabling a better understanding of complex data. Infographics and knowledge graphs have gained significant popularity in recent years due to their effectiveness in conveying information. The review starts by exploring the foundations of graph theory, covering key concepts, algorithms, and applications. It discusses the different types of graphs, including directed, undirected, weighted, and bipartite graphs, and their specific use cases in scientific studies. Special attention is given to special graphs, such as complete graphs, trees, and social networks, which have unique properties and play a significant role in various scientific domains. The review showcases their applications and contributions in fields like biology, social sciences, network analysis, and data mining. Graph visualization emerges as a crucial aspect of understanding and interpreting complex data structures. The review emphasizes the challenges and advancements in graph visualization techniques, enabling researchers to effectively communicate and analyze graph-based information. In conclusion, this comprehensive review serves as a valuable resource for researchers in understanding the principles and applications of graph theory in scientific studies. The exploration of graph types, special graphs, and graph visualization techniques provides insights into the diverse uses and potential of graphs in various scientific disciplines.Öğe A key review on security and privacy of big data: issues, challenges, and future research directions(Springer London Ltd, 2023) Demiroll, Doygun; Das, Resul; Hanbay, DavutBig data collection means collecting large volumes of data to have insight into better business decisions and greater customer satisfaction. Securing big data is difficult not just because of the large amount of data it handles, but also because of the continuous streaming of data, multiple types of data, and cloud-based data storage. Additionally, traditional security and privacy methods are insufficient due to large data volume, speed, and data from different sources. In this study, we focused on the security and privacy issues of big data. To this end, many related articles published in the literature have been extensively examined. Moreover, within the scope of big data, data security and privacy practices, current technologies, and methods are presented. Furthermore, we present an analysis of the security requirements of big data and the elimination of security vulnerabilities against cyber attacks.Öğe Knowledge Graph Visualization: Methods, Interactivity and Practical Considerations(Institute of Electrical and Electronics Engineers Inc., 2025) Soylu, Mucahit; Das, ResulWhile knowledge graphs are invaluable to fields such as artificial intelligence and data analysis, their size and complexity make them difficult to understand. This study examines how we can make large and complex knowledge graphs more understandable, that is, how we can effectively 'visualize' them. First, we cover basic visualization methods such as node-link diagrams and force-oriented layouts. We compare which method works best when, as well as their advantages and disadvantages. We then focus on interactive techniques that enable users to explore the data independently, beyond simply viewing a static image. We show how features such as filtering, clustering, and zooming simplify and enrich the analysis process with examples. Finally, we discuss today's challenging problems, such as reducing visual clutter and displaying constantly changing (dynamic) data. We conclude by offering a perspective on what opportunities new technologies, such as augmented and virtual reality, can offer in this area. This paper aims to provide both a theoretical foundation and a practical roadmap for anyone interested in knowledge graph visualization. © 2025 IEEE.Öğe A new approach to recognizing the use of attitude markers by authors of academic journal articles(Pergamon-Elsevier Science Ltd, 2023) Soylu, Mucahit; Soylu, Ayfer; Das, ResulThis study investigates the use of Attitude Markers(AMs) by native academic authors of English (NAAEs) and Turkish-speaking academic authors of English (TAAEs) in 100 academic articles on Teacher Education. The primary objectives are to examine the forms and functions of AMs used by both groups to indicate their stance in articles and to compare the frequency and variety of AMs used by each group. The study employs a corpus -based approach and adopts a graph visualization method to present the findings. The data were cleaned using a software-supported approach to improve the efficiency of corpus compilation. The data were analyzed using the Antconc text analysis tool (Anthony, 2011) and Log-likelihood statistics. The reliability of the analysis was tested by calculating the inter-rater reliability. To do this, the content coded by one of the researchers and an independent rater was compared using Cohen's Kappa coefficient. The results ranged from 0.81 to 0.92, indicating a high level of reliability. Later, the findings were visualized using a radial knowledge graph. The statistical analysis showed significant differences in the use of certain AMs between the two groups, including AMs related to assessment(-13,20 LL; p<.01) and significance(-82,64 LL; p<.01). The findings indicate that both NAAEs and TAAEs commonly use AMs to convey their stance, with 'significance' and 'assessment' being the most frequent functional categories, and 'adjective' being the most commonly used form of AMs in both corpora. The findings provide valuable insights into the use of AMs in academic writing and can inform the development of English for academic purposes (EAP) course materials to enhance the academic writing skills of novice writers. The results of the study suggest that future research could use graph visualization to carry our corpus studies and could explore the effectiveness of using Artificial Intelligence (AI) technologies to minimize human bias in qualitative analyses.Öğe NOVEL NETWORK STEGANOGRAPHY APPROACHES FOR CONFIDENTIAL TRANSMISSION OF INFORMATION(Ieee, 2017) Karadogan, Ismail; Das, Resul; Karci, AliIn this paper, two different packet timing and IP ID based network steganography approaches have been proposed to provide confidential transmission of information. In the proposed packet timing approach, it is seen that the bandwidth is higher than the previous methods. The proposed IP ID field based approach has been found to be more effective than previous methods in ensuring the uniqueness of the ID field and is suitable for sending confidential data over the Internet. In addition, comprehensive information on this study was presented by examining methods of information hiding, hidden channels and secret data transmission in the network environment.Öğe A Perspective Bakes Your Ballistic Power(Ieee, 2019) Demirol, Doygun; Das, Resul; Hanbay, DavutData, which is the new trend of 21st century, has made difficult to extract meaningful information from itself because of its increasing volume and variety Big data platforms and tools to analyze the size and type of data that cannot be processed by traditional methods have brought new perspectives to data analytics. Big Data are gathered from non-traditional sources such as wireless sensors, blogs, social media, emails etc. In addition, companies can make better insights of large data sets by using large data analysis mechanisms. In this study, the definition of big data, its relationship with other technologies, technologies used in big data area and general information about analysis techniques are given.Öğe Prediction and graph visualization of cyber attacks using graph attention networks(Elsevier Advanced Technology, 2025) Soylu, Mucahit; Das, ResulThis study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.
