| مشخصات مقاله | |
| ترجمه عنوان مقاله | یک سیستم جدید تشخیص نفوذ مبتنی بر مجموعه برای اینترنت اشیا |
| عنوان انگلیسی مقاله | A New Ensemble-Based Intrusion Detection System for Internet of Things |
| انتشار | مقاله سال 2021 |
| تعداد صفحات مقاله انگلیسی | 15 صفحه |
| هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
| پایگاه داده | نشریه اسپرینگر |
| نوع نگارش مقاله |
مقاله پژوهشی (Research article) |
| مقاله بیس | این مقاله بیس نمیباشد |
| نمایه (index) | JCR – Master Journal List – Scopus – ISC |
| نوع مقاله | ISI |
| فرمت مقاله انگلیسی | |
| ایمپکت فاکتور(IF) |
2.334 در سال 2020 |
| شاخص H_index | 43 در سال 2020 |
| شاخص SJR | 0.360 در سال 2020 |
| شناسه ISSN | 2193-567X |
| شاخص Quartile (چارک) | Q2 در سال 2020 |
| فرضیه | ندارد |
| مدل مفهومی | ندارد |
| پرسشنامه | ندارد |
| متغیر | ندارد |
| رفرنس | دارد |
| رشته های مرتبط | مهندسی کامپیوتر، مهندسی فناوری اطلاعات |
| گرایش های مرتبط | اینترنت و شبکه های گسترده، شبکه های کامپیوتری، امنیت اطلاعات |
| نوع ارائه مقاله |
ژورنال |
| مجله / کنفرانس | مجله عربی علوم و مهندسی – Arabian Journal for Science and Engineering |
| دانشگاه | Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan |
| کلمات کلیدی | تشخیص نفوذ، اینترنت اشیا، یادگیری ماشینی، امنیت، تشخیص ناهنجاری، یادگیری گروهی |
| Intrusion detection – IoT – Machine learning – Security – Anomaly detection – Ensemble learning | |
| شناسه دیجیتال – doi |
https://doi.org/10.1007/s13369-021-06086-5 |
| کد محصول | E15965 |
| وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
| دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
| سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
| فهرست مطالب مقاله: |
| Abstract Introduction Literature Review Methodologies Dataset Results Conclusion Conclusion References |
| بخشی از متن مقاله: |
| 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]. |