SAutoIDS: a semantic autonomous intrusion detection system based on cellular deep learning and ontology for malware detection in cloud computing

SAutoIDS: a semantic autonomous intrusion detection system based on cellular deep learning and ontology for malware detection in cloud computing

연구 분야: Safety

학회: International Journal of Information Technology

Penetration of malware in mobile devices causes loss of information or theft of mobile data. Today, various methods have been proposed to malware detection in Cloud computing. In this paper, a semantic autonomous intrusion detection system (SAutoIDS) based on the ontology and cellular automata (CLA) and group method of data handling deep neural network (GMDH-DNN) is proposed to malware detection. The Semantic Multi-Level Approach (SMLA) processes of the data and transformed into semantic values based on a semantic level. The ontology method selects optimal features from malware data. Then the semantic data are divided into training and testing samples. Training data are implemented to the GMDH-DNN for creating the model and CLA to optimize the GMDH model. Finally, testing data are entered into the optimized GMDH model and malwares are detected. We have used CICMaldroid2020, malicious, ArvindMahindru66 and Android Applications dataset to evaluate the SAutoIDS. By implementing the SAutoIDS, it was observed that the accuracy improved by 1.1% compared to other methods MAFF, Federation, Machine learning and De-LADY Method.