Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme  

作　　者：Manal Abdullah Alohali Muna Elsadig Fahd N.Al-Wesabi Mesfer Al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel 

机构地区：[1]Department of Information Systems,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [2]Department of Computer Science,College of Science&Art at Mahayil,King Khalid University,Abha,62529,Saudi Arabia [3]Department of Computer Science,College of Sciences and Humanities-Aflaj,Prince Sattam bin Abdulaziz University,Al-Kharj 16278,Saudi Arabia [4]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University Al-Kharj,16278,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第9期3583-3598,共16页计算机系统科学与工程（英文）

基　　金：This work was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant No.(RGP-1443-0051)。

摘　　要：With recent advancements in information and communication technology,a huge volume of corporate and sensitive user data was shared consistently across the network,making it vulnerable to an attack that may be brought some factors under risk:data availability,confidentiality,and integrity.Intrusion Detection Systems(IDS)were mostly exploited in various networks to help promptly recognize intrusions.Nowadays,blockchain(BC)technology has received much more interest as a means to share data without needing a trusted third person.Therefore,this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification(BAOML-CADC)technique.In the BAOML-CADC technique,the major focus lies in identifying cyberattacks.To do so,the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection(TEA-FS)method for the optimal choice of features.The BAOML-CADC technique uses an extreme learning machine(ELM)model for cyberattack recognition.In addition,a BC-based integrity verification technique is developed to defend against the misrouting attack,showing the innovation of the work.The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset.The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

关 键 词：Cyberattack machine learning blockchain thermal equilibrium algorithm feature selection 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP311.13[自动化与计算机技术—控制科学与工程]