An Enhanced Intrusion Detection System Using Attention-Based Stacked Sparse Autoencoder Feature Extraction
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| Titel: | An Enhanced Intrusion Detection System Using Attention-Based Stacked Sparse Autoencoder Feature Extraction |
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| Autoren: | Venkata Ramani Varanasi, Razia Shaik |
| Quelle: | Engineering, Technology & Applied Science Research. 15:24436-24441 |
| Verlagsinformationen: | Engineering, Technology & Applied Science Research, 2025. |
| Publikationsjahr: | 2025 |
| Beschreibung: | Attention-based stacked sparse autoencoders (AB-SSAEs) are an innovative method for improving Intrusion Detection Systems (IDSs) through the extraction of important features in high-dimensional and heterogeneous data. The proposed AB-SSAE presents an innovative approach to optimizing feature extraction processes using attention mechanisms and a hierarchy of focused sparse autoencoders. The AB-SSAE architecture employs several layers of sparse autoencoders, which transform features through attention mechanisms at every level, improving precision for feature extraction. AB-SSAE employs adaptive denoising with median filtering as a preprocessing step. From the mined data, normal and intrusion attempts are efficiently classified using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network. The proposed technique was compared with several existing approaches, and the results showed that it can differentiate between malicious and benign network traffic with an accuracy of over 0.98. |
| Publikationsart: | Article |
| ISSN: | 1792-8036 2241-4487 |
| DOI: | 10.48084/etasr.11034 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi...........76c00c7645256a3834b66e1e064e262c |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Attention-based stacked sparse autoencoders (AB-SSAEs) are an innovative method for improving Intrusion Detection Systems (IDSs) through the extraction of important features in high-dimensional and heterogeneous data. The proposed AB-SSAE presents an innovative approach to optimizing feature extraction processes using attention mechanisms and a hierarchy of focused sparse autoencoders. The AB-SSAE architecture employs several layers of sparse autoencoders, which transform features through attention mechanisms at every level, improving precision for feature extraction. AB-SSAE employs adaptive denoising with median filtering as a preprocessing step. From the mined data, normal and intrusion attempts are efficiently classified using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network. The proposed technique was compared with several existing approaches, and the results showed that it can differentiate between malicious and benign network traffic with an accuracy of over 0.98. |
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| ISSN: | 17928036 22414487 |
| DOI: | 10.48084/etasr.11034 |