An efficient self attention-based 1D-CNN-LSTM Network for IoT attack detection and identification using network traffic
| dc.contributor.advisor | Lu, Rongxing | |
| dc.contributor.advisor | Lashkari, Arash Habibi | |
| dc.contributor.author | Sasi, Tinshu | |
| dc.date.accessioned | 2024-08-20T14:03:55Z | |
| dc.date.available | 2024-08-20T14:03:55Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | In the last 10 years, the Internet of Things (IoT) has played a crucial role in the digital transformation of society. However, it is also facing increased security vulnerabilities because of the wide range of devices it encompasses. This research presents a novel mechanism called the Self Attention-Based 1D-CNN-LSTM Network for detecting IoT attacks. The proposed mechanism achieves an impressive accuracy of 99.96% and efficiently differentiates between malicious and benign samples. By employing Shapley Additive Explanations (SHAP), we were able to identify important predictive features from the preprocessed data, which were retrieved using CICFlowmeter. This has strengthened the dependability of the model. In addition, we enhanced the model by training it on a smaller collection of features, resulting in shorter training time while preserving accuracy. We have also generated novel IoT tabular datasets consisting of nine widely accessible IoT datasets, as specified in Table 5.1, to evaluate the model’s robustness and showcase its efficacy in IoT security. | |
| dc.description.copyright | © Tinshu Sasi, 2024 | |
| dc.format.extent | xiv, 114 | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/38077 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.title | An efficient self attention-based 1D-CNN-LSTM Network for IoT attack detection and identification using network traffic | |
| dc.type | master thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.C.S. |