Exploring Impact of Data Noise on IoT Security: a Study using Decision Tree Classification in Intrusion Detection Systems | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 4، بهمن 2023، صفحه 609-626 اصل مقاله (1.1 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.12964.2442 | ||
| نویسندگان | ||
| S. Mojtaba Matinkhah* ؛ Roya Morshedi؛ Akbar Mostafavi | ||
| Department of Computer Engineering, Yazd University, Yazd, Iran. | ||
| چکیده | ||
| The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has become critical for ensuring the security and privacy of IoT networks. This article presents a study that evaluates the accuracy of an intrusion detection system (IDS) for detecting network attacks in the Internet of Things (IoT) network. The proposed IDS uses the Decision Tree Classifier and is tested on four benchmark datasets: NSL-KDD, BOT-IoT, CICIDS2017, and MQTT-IoT. The impact of noise on the training and test datasets on classification accuracy is analyzed. The results indicate that clean data has the highest accuracy, while noisy datasets significantly reduce accuracy. Furthermore, the study finds that when both training and test datasets are noisy, the accuracy of classification decreases further. The findings of this study demonstrate the importance of using clean data for training and testing an IDS in IoT networks to achieve accurate classification. This research provides valuable insights for the development of a robust and accurate IDS for IoT networks. | ||
| کلیدواژهها | ||
| Classification Accuracy؛ Clean Data؛ Decision Tree Classifier؛ Intrusion Detection System؛ IoT Networks | ||
| مراجع | ||
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