Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques
The increasing relevance of anomaly detection in attributed networks is gaining traction in fields such as cybersecurity, finance, and healthcare. However, large-scale attributed networks often exhibit noisy and inconsistent node properties, which negatively affect anomaly detection accuracy and dis...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10898008/ |
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| author | Wasim Khan Mohammad Ishrat Mohammad Nadeem Ahmed Shafiqul Abidin Mohammad Husain Mohd Izhar Abu Taha Zamani Mohammad Rashid Hussain Arshad Ali |
| author_facet | Wasim Khan Mohammad Ishrat Mohammad Nadeem Ahmed Shafiqul Abidin Mohammad Husain Mohd Izhar Abu Taha Zamani Mohammad Rashid Hussain Arshad Ali |
| author_sort | Wasim Khan |
| collection | DOAJ |
| description | The increasing relevance of anomaly detection in attributed networks is gaining traction in fields such as cybersecurity, finance, and healthcare. However, large-scale attributed networks often exhibit noisy and inconsistent node properties, which negatively affect anomaly detection accuracy and disrupt the network’s structure. A key challenge is maintaining the integrity of both network and node feature structures during the embedding process. To address this, we propose a novel approach that combines a Graph Convolution Auto encoder (GCAE) with self-supervised learning, proximity preservation, and adversarial training using Generative Adversarial Networks (GAN). First, Laplacian smoothing is applied to reduce noise in node properties, followed by Laplacian sharpening to highlight important features. These enhanced features are then fed into the GCAE, which encodes node attributes into a latent space using graph convolutional layers. Self-supervised tasks like attribute masking and edge prediction further enhance the GCAE’s ability to capture the graph’s structure. Additionally, proximity preservation ensures that the latent space reflects both first order and high-order proximity. The inclusion of GAN refines the embeddings, aligning them closer to the true distribution of the graph data. This method effectively preserves both node features and network structure, making the embedding robust and distinguishable. Empirical evaluations on four real-world datasets demonstrate that our approach surpasses state-of-the-art methods, setting a new benchmark for anomaly detection in attributed networks. Our framework has significant potential to advance both research and practical applications in anomaly detection. |
| format | Article |
| id | doaj-art-fa97343b0d814a85a2b444bf9e99158a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fa97343b0d814a85a2b444bf9e99158a2025-08-20T02:30:32ZengIEEEIEEE Access2169-35362025-01-0113427774279610.1109/ACCESS.2025.354426010898008Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding TechniquesWasim Khan0https://orcid.org/0000-0003-2311-1451Mohammad Ishrat1https://orcid.org/0000-0002-9699-4454Mohammad Nadeem Ahmed2https://orcid.org/0000-0003-1602-0770Shafiqul Abidin3Mohammad Husain4https://orcid.org/0000-0001-7312-9567Mohd Izhar5Abu Taha Zamani6https://orcid.org/0000-0002-1424-487XMohammad Rashid Hussain7https://orcid.org/0000-0001-5035-274XArshad Ali8https://orcid.org/0000-0001-5625-0867Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, Maharashtra, IndiaKoneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, IndiaDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi ArabiaAligarh Muslim University, Aligarh, Uttar Pradesh, IndiaFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaDr. Akhilesh Das Gupta Institute of Professional Studies (ADGIPS), New Delhi, IndiaDepartment of Computer Science, Faculty of Science, Northern Border University, Arar, Saudi ArabiaDepartment of Business Informatics, College of Business, King Khalid University, Abha, Saudi ArabiaFaculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaThe increasing relevance of anomaly detection in attributed networks is gaining traction in fields such as cybersecurity, finance, and healthcare. However, large-scale attributed networks often exhibit noisy and inconsistent node properties, which negatively affect anomaly detection accuracy and disrupt the network’s structure. A key challenge is maintaining the integrity of both network and node feature structures during the embedding process. To address this, we propose a novel approach that combines a Graph Convolution Auto encoder (GCAE) with self-supervised learning, proximity preservation, and adversarial training using Generative Adversarial Networks (GAN). First, Laplacian smoothing is applied to reduce noise in node properties, followed by Laplacian sharpening to highlight important features. These enhanced features are then fed into the GCAE, which encodes node attributes into a latent space using graph convolutional layers. Self-supervised tasks like attribute masking and edge prediction further enhance the GCAE’s ability to capture the graph’s structure. Additionally, proximity preservation ensures that the latent space reflects both first order and high-order proximity. The inclusion of GAN refines the embeddings, aligning them closer to the true distribution of the graph data. This method effectively preserves both node features and network structure, making the embedding robust and distinguishable. Empirical evaluations on four real-world datasets demonstrate that our approach surpasses state-of-the-art methods, setting a new benchmark for anomaly detection in attributed networks. Our framework has significant potential to advance both research and practical applications in anomaly detection.https://ieeexplore.ieee.org/document/10898008/Anomaly detectionproximity preservationself-supervised learningLaplacian smoothingLaplacian sharpeninggenerational adversarial network |
| spellingShingle | Wasim Khan Mohammad Ishrat Mohammad Nadeem Ahmed Shafiqul Abidin Mohammad Husain Mohd Izhar Abu Taha Zamani Mohammad Rashid Hussain Arshad Ali Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques IEEE Access Anomaly detection proximity preservation self-supervised learning Laplacian smoothing Laplacian sharpening generational adversarial network |
| title | Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques |
| title_full | Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques |
| title_fullStr | Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques |
| title_full_unstemmed | Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques |
| title_short | Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques |
| title_sort | enhancing anomaly detection in attributed networks using proximity preservation and advanced embedding techniques |
| topic | Anomaly detection proximity preservation self-supervised learning Laplacian smoothing Laplacian sharpening generational adversarial network |
| url | https://ieeexplore.ieee.org/document/10898008/ |
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