A Deep Random Forest Model on Spark for Network Intrusion Detection
المؤلفون المشاركون
Liu, Zhenpeng
Su, Nan
Qin, Yiwen
Lu, Jiahuan
Li, Xiaofei
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-22
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
This paper focuses on an important research problem of cyberspace security.
As an active defense technology, intrusion detection plays an important role in the field of network security.
Traditional intrusion detection technologies have problems such as low accuracy, low detection efficiency, and time consuming.
The shallow structure of machine learning has been unable to respond in time.
To solve these problems, the deep learning-based method has been studied to improve intrusion detection.
The advantage of deep learning is that it has a strong learning ability for features and can handle very complex data.
Therefore, we propose a deep random forest-based network intrusion detection model.
The first stage uses a slide window to segment original features into many small pieces and then trains a random forest to generate the concatenated class vector as rerepresentation.
The vector will be used to train the multilevel cascade parallel random forest in the second stage.
Finally, the classification of the original data is determined by voting strategy after the last layer of cascade.
Meanwhile, the model is deployed in Spark environment and optimizes cache replacement strategy of RDDs by efficiency sorting and partition integrity check.
The experiment results indicate that the proposed method can effectively detect anomaly network behaviors, with high F1-measure scores and high accuracy.
The results also show that it can cut down the average execution time on different scaled clusters.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Zhenpeng& Su, Nan& Qin, Yiwen& Lu, Jiahuan& Li, Xiaofei. 2020. A Deep Random Forest Model on Spark for Network Intrusion Detection. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1192440
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Zhenpeng…[et al.]. A Deep Random Forest Model on Spark for Network Intrusion Detection. Mobile Information Systems No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1192440
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Zhenpeng& Su, Nan& Qin, Yiwen& Lu, Jiahuan& Li, Xiaofei. A Deep Random Forest Model on Spark for Network Intrusion Detection. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1192440
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1192440
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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