TY - EJOU AU - Alohali, Manal Abdullah AU - Elsadig, Muna AU - Al-Wesabi, Fahd N. AU - Duhayyim, Mesfer Al AU - Hilal, Anwer Mustafa AU - Motwakel, Abdelwahed TI - Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme T2 - Computer Systems Science and Engineering PY - 2023 VL - 46 IS - 3 SN - AB - 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. KW - Cyberattack; machine learning; blockchain; thermal equilibrium algorithm; feature selection DO - 10.32604/csse.2023.037545