Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment  

作　　者：Z.Faizal Khan Saeed M.Alshahrani Abdulrahman Alghamdi Someah Alangari Nouf Ibrahim Altamami Khalid A.Alissa Sana Alazwari Mesfer Al Duhayyim Fahd N.Al-Wesabi 

机构地区：[1]Department of Computer Engineering,College of Computing and Information Technology,Shaqra University,Shaqra,Saudi Arabia [2]Department of Computer Science,College of Computing and Information Technology,Shaqra University,Shaqra,Saudi Arabia [3]Department of Computer Science,College of Science and Humanities Dawadmi,Shaqra University,Shaqra,Saudi Arabia [4]Department of Mathematics,College of Education,Shaqra University,Shaqra,Saudi Arabia [5]Saudi Aramco Cybersecurity Chair,Networks and Communications Department,College of Computer Science and Information Technology,Imam Abdulrahman Bin Faisal University,P.O.Box 1982,Dammam,31441,Saudi Arabia [6]Department of Information Technology,College of Computers and Information Technology,Taif University,Taif P.O.Box 11099,Taif,21944,Saudi Arabia [7]Department of Computer Science,College of Sciences and Humanities-Aflaj,Prince Sattam bin Abdulaziz University,Alaflaj,16828,Saudi Arabia [8]Department of Computer Science,College of Science&Art at Mahayil,King Khalid University,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第10期855-871,共17页计算机系统科学与工程（英文）

基　　金：The authors extend their appreciation to the deanship of scientific research at Shaqra University for funding this research work through the project number(SU-NN-202210).

摘　　要：The Internet of Things(IoT)is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare,in health service to energy,and in developed to transport.Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved.The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence(AI)andMachine Learning(ML)devices are crucial fact to realize security in IoT platform.It can be required for minimizing the issues of security based on IoT devices efficiently.Thus,this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection fromthe cloud and IoT environments.The proposed MFORELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms.To accomplish this,the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format.Besides,the proposed models will receive the pre-processing data and carry out the classifier method.For boosting the efficiency of the proposed models,theMFOtechnique was utilized to it.The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset,and the outcomes are examined under distinct aspects.

关 键 词：Mayfly optimization machine learning artificial intelligence CYBERSECURITY threat detection 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]