کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13429348 | 1842324 | 2020 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Data-driven software defined network attack detection : State-of-the-art and perspectives
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
SDN (Software Defined Network) has emerged as a revolutionary technology in network, a substantial amount of researches have been dedicated to security of SDNs to support their various applications. The paper firstly analyzes State-of-the-Art of SDN security from data perspectives. Then some typical network attack detection (NAD) methods are surveyed, including machine learning based methods and statistical methods. After that, a novel tensor based network attack detection method named tensor principal component analysis (TPCA) is proposed to detect attacks. After surveying the last data-driven SDN frameworks, a tensor based big data-driven SDN attack detection framework is proposed for SDN security. In the end, a case study is illustrated to verify the effectiveness of the proposed framework.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 513, March 2020, Pages 65-83
Journal: Information Sciences - Volume 513, March 2020, Pages 65-83
نویسندگان
Puming Wang, Laurence T. Yang, Xin Nie, Zhian Ren, Jintao Li, Liwei Kuang,