Until now, an effective defense method against Distributed Denial of Service (DDoS) attacks is yet to be offered by security systems. Incidents of serious damage due to DDoS attacks have been increasing, thereby leading to an urgent need for new attack identification, mitigation, and prevention mechanisms. To prevent DDoS attacks, the basic features of the attacks need to be dynamically analyzed because their patterns, ports, and protocols or operation mechanisms are rapidly changed and manipulated. Most of the proposed DDoS defense methods have different types of drawbacks and limitations. Some of these methods have signature-based defense mechanisms that fail to identify new attacks and others have anomaly-based defense mechanisms that are limited to specific types of DDoS attacks and yet to be applied in open environments. Subsequently, extensive research on applying artificial intelligence and statistical techniques in the defense methods has been conducted in order to identify, mitigate, and prevent these attacks. However, the most appropriate and effective defense features, mechanisms, techniques, and methods for handling such attacks remain to be an open question. This review paper focuses on the most common defense methods against DDoS attacks that adopt artificial intelligence and statistical approaches. Additionally, the review classifies and illustrates the attack types, the testing properties, the evaluation methods and the testing datasets that are utilized in the methodology of the proposed defense methods. Finally, this review provides a guideline and possible points of encampments for developing improved solution models of defense methods against DDoS attacks.

 “High Resolution Systems for Automated Diagnosis of Hepatitis "(1) (SCEE2018)  ،IEEE.

)“Modelling For Predicting the Severity of Hepatitis Based On Artificial Neural Network” Journal of Engineering and Applied Sciences. 2018 (indexed in scopus, citescore: 0.31)

Elaina, N. S., Malik, A. S., Shams, W. K., Badruddin, N., Abdullah, J. M., & Reza, M. F. (2018). Localized N20 Component of Somatosensory Evoked Magnetic Fields in Frontoparietal Brain Tumor Patients Using Noise-Normalized Approaches. Clinical Neuroradiology, 1-15. ISI,Q1,IF 2.8