4.5 Article

A New Ensemble-Based Intrusion Detection System for Internet of Things

期刊

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
卷 47, 期 2, 页码 1805-1819

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s13369-021-06086-5

关键词

Intrusion detection; IoT; Machine learning; Security; Anomaly detection; Ensemble learning

资金

  1. Politecnico di Bari within the CRUI-CARE Agreement

向作者/读者索取更多资源

The IoT domain has evolved significantly in recent years, transforming human lives through automation of daily tasks. In response to the increasing cyber threats in IoT networks, there is a need to enhance intrusion detection systems. This study proposes an ensemble-based intrusion detection model leveraging machine learning techniques, which shows significant improvements in performance compared to existing models.
The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model's performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据