Stiawan, Deris (2022) Feature Selection using Chi Square to Improve Attack Detection Classification in IoT Network: Work in Progress (Similarity). Turnitin Universitas Sriwijaya. (Submitted)
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Abstract
To maintain network security, Intrusion Detection System (IDS) is needed to detect anomaly and attack. Designing proper IDS requires accurate model. This paper proposes a model, which consists of statistical extraction, feature selection, dataset clustering, classification, and performance measurement. Experiments on MQTT-IOT-IDS2020 dataset which contains Normal, scan_A, scan_sU, Sparta and mqtt_bruteforce are conducted. The dataset is statistically extracted using Bidirectional-based features packet header feature with 37 features. Chi square algorithm is selected for performing feature extraction process. 10 relevant and best features are selected and ranked into 5-subsets and 10-subset feature. Three dataset splitting into testing data and training data of 90%:10%, 70%:30% and 50%:50% are created. Binary classification using k-Nearest Neighbor (KNN) and Adaboost algorithms are performed. The experimental results show accuracy level above 99% for all scenarios, with Adaboost algorithm outperforms k-Nearest Neighbor algorithm.
| Item Type: | Other |
|---|---|
| Subjects: | #3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker |
| Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
| Depositing User: | Dr. Deris Stiawan |
| Date Deposited: | 26 Nov 2022 09:37 |
| Last Modified: | 26 Nov 2022 09:37 |
| URI: | http://repository.unsri.ac.id/id/eprint/82364 |
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