An anomaly based approach for DDoS attacks detection in cloud environment
العناوين الأخرى
نموذج لاكتشاف هجومات الحرمان من الخدمة في بيئة الحوسبة السحابية
مقدم أطروحة جامعية
al-Hawawrih, Muna Sulayman Ali
مشرف أطروحة جامعية
أعضاء اللجنة
al-Nuaymat, Ghazi Muhammad
Hammad, Mustafa Muhammad
al-Nabhan, Muhammad
الجامعة
جامعة مؤتة
الكلية
كلية تكنولوجيا المعلومات
القسم الأكاديمي
قسم الحاسوب
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2016
الملخص الإنجليزي
Cloud computing has been the biggest Information Technology and industry buzzword in recent years, and will continue to be so for the foreseeable future.
It has drawn significant attention from researchers due to its widespread application and substantial benefits.
Because of its distributed nature–specifically, using virtualization, multi-tenant and their reliance on the Internet to provide their services, security poses a major threat to cloud computing.
Currently, an insider Distributed Denial of Service (DDoS) attack is the biggest challenge for a cloud environment, where the unavailability of services and connectivity issues in the cloud can deactivate the services, which takes an immense toll in terms of business and financial losses for consumers.
Hence, to protect the cloud environment–in particular, the virtual environment–from DDoS activities, we need more than a traditional defense mechanism such as firewalls, which sniff the network packets at the boundary of the network to detect and prevent the attacks from entering the network, but are incapable of detecting insider attacks.
Intrusion Detection Systems (IDS) are an important key to cloud infrastructure security.
This work proposes an anomaly intrusion detection approach in the hypervisor layer to discourage DDoS activities between virtual machines.
The proposed approach is implemented by the evolutionary neural network, which integrates the particle swarm optimization with neural network for detection and classification of the traffic that is exchanged between virtual machines.
Here, the particle swarm optimization is used to choose the optimal weights for neural network to achieve a high accuracy.
Our aim is to ensure the feasibility of the proposed model in detecting DDoS attacks in the virtual cloud.
Seeing as there is currently no available dataset for testing and validating the cloud intrusion detection system, in this work, a new dataset that contains two types of popular DDoS attacks, TCP-SYN and UDP flood attacks are generated.
The performance analysis and results showed that the proposed intrusion detection approach achieved a high accuracy rate, with the best performance being 99.99%, and a false alarm rate of only 0.01%.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
عدد الصفحات
89
قائمة المحتويات
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction
Chapter Two : Literature review.
Chapter Three : Design and methodology.
Chapter Four : Experiment discussion, conclusion and recommendations.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Hawawrih, Muna Sulayman Ali. (2016). An anomaly based approach for DDoS attacks detection in cloud environment. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-749331
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Hawawrih, Muna Sulayman Ali. An anomaly based approach for DDoS attacks detection in cloud environment. (Master's theses Theses and Dissertations Master). Mutah University. (2016).
https://search.emarefa.net/detail/BIM-749331
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Hawawrih, Muna Sulayman Ali. (2016). An anomaly based approach for DDoS attacks detection in cloud environment. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-749331
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-749331
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