@Article{csse.2023.037545, AUTHOR = {Manal Abdullah Alohali, Muna Elsadig, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel}, TITLE = {Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme}, JOURNAL = {Computer Systems Science and Engineering}, VOLUME = {46}, YEAR = {2023}, NUMBER = {3}, PAGES = {3583--3598}, URL = {http://www.techscience.com/csse/v46n3/52208}, ISSN = {}, ABSTRACT = {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.}, DOI = {10.32604/csse.2023.037545} }