Intelligent intrusion detection framework for multi-clouds – IoT environment using swarm-based deep learning classifier

Abstract In the current era, a tremendous volume of data has been generated by using web technologies. The association between different devices and services have also explored to wisely widely use recent Due restriction in available resources, chance security violation is increasing highly on constrained devices. IoT backend with multi-cloud infrastructure extend public terms better scalability reliability. Several users might access resources that lead threats while handling user requests for services. It poses new challenge proposing functional elements schemes. This paper introduces an intelligent Intrusion Detection Framework (IDF) detect network application-based attacks. proposed framework three phases: pre-processing, feature selection classification. Initially, collected datasets are pre-processed Integer- Grading Normalization (I-GN) technique ensures fair-scaled transformation process. Secondly, Opposition-based Learning- Rat Inspired Optimizer (OBL-RIO) designed phase. progressive nature rats chooses significant features. fittest value stability features from OBL-RIO. Finally, 2D-Array-based Convolutional Neural Network (2D-ACNN) as binary class classifier. input preserved 2D-array model perform complex layers. detects normal (or) abnormal traffic. trained tested Netflow-based datasets. yields 95.20% accuracy, 2.5% false positive rate 97.24% detection rate.