EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network
المؤلفون المشاركون
Latif, Rabia
Abbas, Haider
Latif, Seemab
Masood, Ashraf
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-10-05
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Due to the scattered nature of DDoS attacks and advancement of new technologies such as cloud-assisted WBAN, it becomes challenging to detect malicious activities by relying on conventional security mechanisms.
The detection of such attacks demands an adaptive and incremental learning classifier capable of accurate decision making with less computation.
Hence, the DDoS attack detection using existing machine learning techniques requires full data set to be stored in the memory and are not appropriate for real-time network traffic.
To overcome these shortcomings, Very Fast Decision Tree (VFDT) algorithm has been proposed in the past that can handle high speed streaming data efficiently.
Whilst considering the data generated by WBAN sensors, noise is an obvious aspect that severely affects the accuracy and increases false alarms.
In this paper, an enhanced VFDT (EVFDT) is proposed to efficiently detect the occurrence of DDoS attack in cloud-assisted WBAN.
EVFDT uses an adaptive tie-breaking threshold for node splitting.
To resolve the tree size expansion under extreme noise, a lightweight iterative pruning technique is proposed.
To analyze the performance of EVFDT, four metrics are evaluated: classification accuracy, tree size, time, and memory.
Simulation results show that EVFDT attains significantly high detection accuracy with fewer false alarms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Latif, Rabia& Abbas, Haider& Latif, Seemab& Masood, Ashraf. 2015. EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network. Mobile Information Systems،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1072707
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Latif, Rabia…[et al.]. EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network. Mobile Information Systems No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1072707
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Latif, Rabia& Abbas, Haider& Latif, Seemab& Masood, Ashraf. EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network. Mobile Information Systems. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1072707
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1072707
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