<p dir="ltr">As the demand for IoT devices continues to grow, our reliance on networks in daily life increases. Whether we are considering individual users or large multinational companies, networks have become an essential asset for people across various industries. However, this dependence on networks also exposes us to security vulnerabilities when traffic is not adequately filtered. A successful attack on the network could have severe consequences for its users. Therefore, the implementation of a network intrusion detection system (IDS) is crucial to safeguard the well-being of our modern society.</p><p dir="ltr">While AI-based IDS is a new force in the field of intrusion detection, it outperforms some traditional approaches. However, it is not without its flaws. The performance of ML-based IDS decreases when applied to a different dataset than the one it was trained on. This decrease in performance hinders the ML-based IDS's ability to be used in a production environment, as the data generated in a production environment also differs from the data that is used to train the IDS. This paper aims to devise an ML-based IDS that is generalizable to a different environment.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25676388 |
| Date | 26 April 2024 |
| Creators | Zhenyu Wan (18431475) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/A_Meta-Learning_based_IDS/25676388 |
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