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An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things
المؤلفون المشاركون
Yang, Hongyu
Zhang, Jiyong
Zeng, Renyun
Wang, Fengyan
Xu, Guangquan
المصدر
Security and Communication Networks
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-28
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
With the wide application of network technology, the Internet of Things (IoT) systems are facing the increasingly serious situation of network threats; the network threat situation assessment becomes an important approach to solve these problems.
Aiming at the traditional methods based on data category tag that has high modeling cost and low efficiency in the network threat situation assessment, this paper proposes a network threat situation assessment model based on unsupervised learning for IoT.
Firstly, we combine the encoder of variational autoencoder (VAE) and the discriminator of generative adversarial networks (GAN) to form the V-G network.
Then, we obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic.
Besides, we conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training.
Moreover, we carry out the group threat testing with the test dataset containing abnormal network traffic and calculate the threat probability of each test group.
Finally, we obtain the threat situation value (TSV) according to the threat probability and the threat impact.
The simulation results show that, compared with the other methods, this proposed method can evaluate the overall situation of network security threat more intuitively and has a stronger characterization ability for network threats.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yang, Hongyu& Zeng, Renyun& Wang, Fengyan& Xu, Guangquan& Zhang, Jiyong. 2020. An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1208495
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yang, Hongyu…[et al.]. An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things. Security and Communication Networks No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1208495
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yang, Hongyu& Zeng, Renyun& Wang, Fengyan& Xu, Guangquan& Zhang, Jiyong. An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1208495
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208495
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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