Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

作     者：Gul Nawaz Muhammad Junaid Adnan Akhunzada Abdullah Gani Shamyla Nawazish Asim Yaqub Adeel Ahmed Huma Ajab 

作者机构：Department of Information TechnologyThe University of HaripurHaripur22060Pakistan Faculty of Computing and InformaticsUniversity Malaysia SabahSabah88400Malaysia Department of Environmental SciencesCOMSATS University Abbottabad CampusAbbottabad22010Pakistan Department of ChemistryCOMSATS University Abbottabad CampusAbbottabad22010Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第11期

页      面：2157-2178页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the University Malaysia Sabah 

主　　题：Distributed denial of service(DDoS)attacks software-defined networking(SDN) classification deep neural network(DNN) 

摘      要：Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate *** proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)*** centralizes the control plane and separates it from the data *** simplifies a network and eliminates vendor specification of a *** of this open nature and centralized control,SDN can easily become a victim of DDoS *** proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate *** Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)*** proposed DNN model scans the data to find the correlated features and delivers high-quality *** model enhances the security of SDN and has better accuracy as compared to previously proposed *** choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing *** model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss *** accuracy increases to 99.80%as we increase the number of epochs to 100 *** proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed *** can handle a huge amount of structured and unstructured data and can easily solve complex problems.