مشخصات مقاله | |
ترجمه عنوان مقاله | فناوری های یادگیری عمیق و کلان داده برای امنیت اینترنت اشیا (IoT) |
عنوان انگلیسی مقاله | Deep learning and big data technologies for IoT security |
انتشار | مقاله سال 2020 |
تعداد صفحات مقاله انگلیسی | 58 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله مروری (Review Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
3.727 در سال 2019 |
شاخص H_index | 91 در سال 2020 |
شاخص SJR | 0.500 در سال 2019 |
شناسه ISSN | 0140-3664 |
شاخص Quartile (چارک) | Q2 در سال 2019 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر، مهندسی فناوری اطلاعات |
گرایش های مرتبط | هوش مصنوعی، امنیت اطلاعات، اینترنت و شبکه های گسترده |
نوع ارائه مقاله |
ژورنال |
مجله | ارتباطات رایانه ای – Computer Communications |
دانشگاه | Research & Innovation Development, Telekom Research & Development Sdn. Bhd, Selangor, Malaysia |
کلمات کلیدی | یادگیری عمیق، کلان داده، امنیت اینترنت اشیا (IoT) |
کلمات کلیدی انگلیسی | Deep Learning, Big Data, IoT Security |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.comcom.2020.01.016 |
کد محصول | E14634 |
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 1. Introduction 2. Motivation and use cases 3. Background 4. Taxonomy 5. State of the art deep learning for IoT security using big data technologies 6. Open challenges and future directions 7. Conclusion Declaration of Competing Interest Acknowledgements References |
بخشی از متن مقاله: |
Abstract
Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. Introduction The swift growth in emerging technologies such as, sensors, smartphones, 5G communication, and virtual reality leads to innovative applications such as, connected industries, smart city, smart energy, connected automobiles, smart agriculture, connected building complexes, connected health care, smart retail outlets, and smart supply chain, which adversely contribute to the accumulation of massive amounts of data. A study conducted by the National Cable & Telecommunications Association (NCTA) predicts that by 2020, approximately 50.1 Billion Internet of Things (IoT) devices will be connected to the Internet. The growth of IoT devices makes the security of these devices debatable [1, 2] According to McAfee (2018), there has been a barrage of cyberattacks and data breaches that has hit almost every industry since 1st of January 2018. Further, many of these attacks were targeted on IoT devices. The increasing use of IoT devices invites the cybercriminals to target them. Additionally, the prospect of interconnectivity among IoT devices makes them vulnerable [3]. |