Modern networks face increasing cyber threats such as malware, ransomware, phishing, DDoS, insider attacks, and advanced persistent threats, making traditional signature-based security systems less effective. Artificial Intelligence (AI), through machine learning and deep learning, enables intelligent threat detection by identifying known and unknown attacks in real time. This study proposes an AI-based threat detection framework that integrates network traffic analysis, preprocessing, feature engineering, threat classification, and automated response. Experimental evaluation using metrics such as accuracy, precision, recall, F1-score, false positive rate, and detection latency demonstrates that the proposed framework outperforms conventional methods by providing higher detection accuracy, lower false alarms, and faster response. Despite challenges related to data quality, model interpretability, and computational cost, AI-driven cybersecurity offers a scalable and effective solution for modern network security.
Seppo Linnainmaa, A. Salomaa· International Journal of Mod...· 0 citations
Graph Foundation Models (GFMs) enable universal representation learning from heterogeneous graph-structured data through large-scale self-supervised pretraining. Unlike traditional Graph Neural Networks (GNNs), GFMs learn transferable structural, semantic, and contextual knowledge across multiple domains, including healthcare, finance, manufacturing, cybersecurity, and smart cities, making them highly effective for cross-domain knowledge integration. This paper presents an intelligent multi-layer GFM framework for integrating heterogeneous knowledge graphs and supporting scalable cross-domain analytics. The framework combines graph representation learning, knowledge graph embedding, transformer-based graph encoders, self-supervised contrastive learning, and domain adaptation to perform semantic alignment, feature extraction, graph embedding optimization, and downstream reasoning within a unified environment. A cross-domain integration pipeline automatically aligns entities, relationships, semantics, and graph topologies from diverse data sources while transformer-based graph attention captures both local and global structural dependencies. The proposed framework is evaluated using metrics such as knowledge integration accuracy, graph embedding accuracy, node classification, link prediction, semantic consistency, computational efficiency, scalability, and inference latency. Experimental results demonstrate improved cross-domain representation learning, enhanced transfer learning, reduced feature engineering, and lower dependence on labeled data compared with conventional graph learning approaches. The framework provides a scalable foundation for graph-based artificial intelligence and has applications in biomedical knowledge discovery, financial fraud detection, industrial digital twins, recommendation systems, cybersecurity intelligence, scientific literature mining, and smart governance. Overall, Graph Foundation Models offer a promising solution for universal graph intelligence, enabling accurate cross-domain reasoning, predictive analytics, and explainable decision-making.
Seppo Linnainmaa, A. Salomaa· International Journal of Eme...· 0 citations