Artificial Immune Algorithm Feature Ontology Based Random Forest Development Cloud Malicious Session Detection
Amlesh singh, Pratima Gautam · Research Square · 2023
Abstract Technology dependency for various work increase computation and that directly enhance the use of cloud in these decades. This computation, storage is a great business for many companies, hence attacker also put a high interest. Many of researcher has proposed different models for the detection and prevention of attacks. This paper has proposed intrusion detection model that work in two step first is creation of feature ontology to train the ransom forest model and second is testing of trained random forest model. For selection of feature proposed odle uses Artificial Immune System genetic algorithm that give good feature set for cloud session class identification. Experiment was done onreal cloud dataset and proposed is able to detect multi class attacks among normal sessions. Results shows that proposed model has increases the accuracy, and other comparing parameters as compared to exsitng models.