Open AccessA Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System
Author(s) -
Suresh Adithya Nallamuthu
Publication year - 2020
Publication title -
journal of computational science and intelligent technologies
Language(s) - English
Resource type - Journals
ISSN - 2582-9041
DOI - 10.53409/mnaa.jcsit20201203
Subject(s) - computer science , intrusion detection system , support vector machine , cloud computing , artificial intelligence , machine learning , artificial neural network , genetic algorithm , classifier (uml) , data mining , false positive rate , precision and recall , network security , metric (unit) , algorithm , pattern recognition (psychology) , computer security , engineering , operations management , operating system
The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.