As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. This work presents a novel framework integrating API call sequences and DLL information into a unified, graph-based representation to analyze malware behavior comprehensively. The proposed model generates initial embeddings using Node2Vec, which uses a random walk approach to understand structural relationships between nodes. Graph Attention Network (GAT) then enhances these initial embeddings, which utilizes attention mechanisms to incorporate contextual dependencies and enhance semantic representations. Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. The dual role of GAT as a classifier and feature extractor is also analyzed to evaluate its impact on embedding quality and classification accuracy. Experimental results show that the proposed model achieves superior results with an accuracy rate of 0.9961 compared to state-of-the-art approaches such as ensemble learning and standalone GAT. This achievement highlights the framework’s ability to utilize contextual information for malware detection. The real-world dataset used provides a benchmark for future work, and this research lays a comprehensive foundation for advancing graph-based malware analysis.

The increasing sophistication of evolving malware types and attack techniques has rendered traditional antivirus solutions inadequate, particularly in mitigating zero-day threats. To address this challenge, Machine Learning (ML) and Deep Learning (DL)-based approaches have been developed, demonstrating significant efficacy and high accuracy in malware classification. However, the black box nature of these models raises significant concerns in terms of transparency and interpretability. This study presents a comprehensive evaluation of Ensemble Learning and Deep Learning methods for static analysis-based malware classification, which allows joint analysis of Application Programming Interface (API) calls and Dynamic Link Library (DLL) data. In the study, a specially designed Convolutional Neural Network (CNN)-Gated Recurrent Units (GRU)-3 model is trained using a tailored dataset consisting of malicious and secure software. In order to better understand the model’s performance, feature importance analysis was performed using SHapley additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) Explainable Artificial Intelligence (XAI) techniques and the reliability of model decisions was increased. The proposed model was compared with DL models such as CNN, Long Short-Term Memory (LSTM), and GRU, as well as traditional ML algorithms such as Extreme Gradient Boosting (XGB), Extra Trees Classifier (ETC), K-Nearest Neighbor (KNN), and Random Forest (RF). While the traditional XGB model achieved the highest overall performance with a 99.81% accuracy rate, our proposed CNN-GRU-3 model demonstrated the best performance among all DL models, reaching a 99.37% accuracy rate. This study presents a powerful framework that provides both high accuracy in malware detection and makes the decision mechanism more transparent.

Neural machine translation (NMT) has achieved remarkable success in high-resource language pairs; however, its effectiveness for morphologically rich and low-resource languages like Turkish remains underexplored. As a highly agglutinative and morphologically complex language with limited high-quality parallel data, Turkish serves as a representative case for evaluating NMT systems on low-resource and linguistically challenging settings. Its structural divergence from English makes it a critical testbed for assessing tokenization strategies, attention mechanisms, and model generalizability in neural translation. This study investigates the comparative performance of two prominent NMT paradigms—the Transformer architecture, and recurrent-based sequence-to-sequence (Seq2Seq) models with attention for both English-to-Turkish and Turkish-to-English translation. The models are evaluated under various configurations, including different tokenization strategies (Byte Pair Encoding (BPE) vs. Word Tokenization), attention mechanisms (Bahdanau and an exploratory hybrid mechanism combining Bahdanau and Scaled Dot-Product attention), and architectural depths (layer count and attention head number). Extensive experiments using automatic metrics such as BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Translation Error Rate (TER) reveal that the Transformer model with three layers, eight attention heads, and BPE tokenization achieved the best performance, obtaining a BLEU score of 47.85 and METEOR score of 44.62 in the English-to-Turkish direction. Similar performance trends were observed in the reverse direction, indicating the model’s generalizability. These findings highlight the potential of carefully optimized Transformer-based NMT systems in handling the complexities of morphologically rich, low-resource languages like Turkish in both translation directions.

The increase in cyber-attacks has also started to threaten the use of internet and information technologies. This situation emphasizes the importance of detecting malicious software that is responsible for cyber-attacks. Nowadays, there are studies on the development of machine learning methods for malicious software detection. Malicious software detectors are the primary tools in defense against malicious software. The quality of such a detector is determined by the techniques it uses. Malware analysis methods such as machine learning, deep learning, and static and dynamic analysis are among these techniques. This study presents malware analysis and classification techniques. For malware detection, well-known algorithms for machine learning including such K-Nearest Neighbors, Naive Bayes, Decision Trees, and Random Forest were used. The research shows that the use of Random Forest classification technique produces the best accuracy with 97.75% classification, while Naive Bayes produces the lowest accuracy of 53%.