<p dir="ltr">This thesis examines machine learning approaches for anomaly detection in network security, particularly focusing on intrusion detection using TCP and UDP protocols. It uses logistic regression models to effectively distinguish between normal and abnormal network actions, demonstrating a strong ability to detect possible security concerns. The study uses the UNSW-NB15 dataset for model validation, allowing a thorough evaluation of the models' capacity to detect anomalies in real-world network scenarios. The UNSW-NB15 dataset is a comprehensive network attack dataset frequently used in research to evaluate intrusion detection systems and anomaly detection algorithms because of its realistic attack scenarios and various network activities.</p><p dir="ltr">Further investigation is carried out using a Multi-Task Neural Network built for binary and multi-class classification tasks. This method allows for the in-depth study of network data, making it easier to identify potential threats. The model is fine-tuned during successive training epochs, focusing on validation measures to ensure its generalizability. The thesis also applied early stopping mechanisms to enhance the ML model, which helps optimize the training process, reduces the risk of overfitting, and improves the model's performance on new, unseen data.</p><p dir="ltr">This thesis also uses blockchain technology to track model performance indicators, a novel strategy that improves data integrity and reliability. This blockchain-based logging system keeps an immutable record of the models' performance over time, which helps to build a transparent and verifiable anomaly detection framework.</p><p dir="ltr">In summation, this research enhances Machine Learning approaches for network anomaly detection. It proposes scalable and effective approaches for early detection and mitigation of network intrusions, ultimately improving the security posture of network systems.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25687656 |
| Date | 02 May 2024 |
| Creators | Vaishnavi Rudraraju (18431880) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/ANOMALY_DETECTION_USING_MACHINE_LEARNING_FORINTRUSION_DETECTION/25687656 |
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