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|Title||Detecting DDoS Attack Using A Multilayer Data Mining techniques|
|Title in Arabic||كشف هجمات حجب الخدمة الموزعة باستخدام تقنية الطبقات المتعددة في تنقيب البيانات|
Availability is one of the three main components of computer security, along with confidentiality and integrity. One of the major threats to network security is Denial of Service (DDoS),which is a relatively simple, but very powerful technique to attack internet resources as well as system resources. Distributed multiple agents consume some critical resources at the target within the short time and deny the service to legitimate clients . Most current network intrusion detection systems employ signature-based methods or supervised-based methods which rely on labelled training data. This training data is typically expensive to produce, these methods have difficulty in detecting new types of attack, Using unsupervised anomaly detection techniques , the system can be trained with unlabelled data and is capable of detecting previously “unseen" attacks. In this research we multi-clustering method using data mining techniques by combination of clustering method (K-Mean(Km) ,K-Medoid(KD),K-Fast Mean(KFM)) as a multi clustering to be able for detecting anew DDoS attacks from unlabelled dataset depend on unsupervised behavior-anomaly detection approach, Davies_Bouldin index(DB) is used to evaluate the proposed method . The results show that the proposed method has lower davies_bouldin index.
|Publisher||الجامعة الإسلامية - غزة|
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