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

al-Kasasibah, Muhammad

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

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-749331