مقاله انگلیسی یک سیستم جدید تشخیص نفوذ مبتنی بر مجموعه برای اینترنت اشیا
Abstract
Introduction
Literature Review
Methodologies
Dataset
Results Conclusion
Conclusion
References
Abstract
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.
Introduction
Today, our planet is surrounded by a plethora of electronic devices that are transforming human lives. In this regard, Internet of Things (IoT) is emerging as an innovative technology that is transforming the industry and life smarter with intelligent devices having enhanced connectivity such as healthcare monitoring, environment monitoring, water management, smart agriculture, and smart home. More precisely in IoT, many heterogeneous physical devices can cooperate and communicate with one another for transferring the data over large number of networks without interference of human-to-human or human-to-device interfaces [1–4]. Figure 1 demonstrates the usage of IoT in different fields. It is anticipated that by year 2025, 41.6 billion IoT devices will be interconnected, which poses many challenges for the practical realization of IoT [5]. Specifically in large IoT networks, where challenges related to the integrity and confidentiality of data exist. The number of security concerns, such as zero-day attacks aimed at internet users, has increased. As a result of the widespread use of the Internet in numerous nations, such as Australia and the USA, zeroday assaults had a considerable impact [6].