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Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

Computers, Materials & Continua 2023年第11期77卷 2157-2178页

作者： Gul Nawaz Muhammad Junaid Adnan Akhunzada Abdullah Gani Shamyla Nawazish Asim Yaqub Adeel Ahmed Huma Ajab Department of Information Technology The University of HaripurHaripur22060Pakistan Faculty of Computing and Informatics University Malaysia SabahSabah88400Malaysia Department of Environmental Sciences COMSATS University Abbottabad CampusAbbottabad22010Pakistan Department of Chemistry COMSATS University Abbottabad CampusAbbottabad22010Pakistan

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.

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

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New Modified Controlled Bat Algorithm for Numerical Optimization Problem

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Computers, Materials & Continua 2022年第2期70卷 2241-2259页

作者： Waqas Haider Bangyal Abdul Hameed Jamil Ahmad Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Joel J.P.C.Rodrigues M.Adil Khan Danda B.Rawat Richard Etengu Department of Computing and Technology Iqra University IslamabadPakistan Computer Science Kohat University of Science and Technology(KUST)KohatPakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Federal university of Piauí(UFPI) Teresina-PLBrazil Instituto de Telecomunicacoes Covilh?6201-001Portugal Department Computer Science and Technology Chang’an UniversityXi’anChina Department of Electrical Engineering and Computer Science Howard UniversityWashingtonDCUSA

Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization *** leverages the echolocation feature of bats produced by imitating the bats’searching *** faces premature convergence due to its local search *** of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search *** this work,we proposed an improved variation of BA by applying torus walk to improve diversity and *** *** Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test *** finding of our technique shows promising performance as compared to the standard PSO and standard *** proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of *** results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional *** MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

关键词： Bat algorithm MCBA ANN ML Torus walk

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