مقاله انگلیسی رایگان در مورد یک سیستم جدید تشخیص نفوذ مبتنی بر مجموعه برای اینترنت اشیا – اسپرینگر ۲۰۲۱

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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 ۲۰۲۱
تعداد صفحات مقاله انگلیسی  ۱۵ 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  PDF
ایمپکت فاکتور(IF)
۲٫۳۳۴ در سال ۲۰۲۰
شاخص H_index ۴۳ در سال ۲۰۲۰
شاخص SJR ۰٫۳۶۰ در سال ۲۰۲۰
شناسه ISSN ۲۱۹۳-۵۶۷X
شاخص Quartile (چارک) Q2 در سال ۲۰۲۰
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.
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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 [1–۴]. 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].

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