Intrusion detection by machine learning for multimedia platform

• Published in: Multimedia tools and applications 2021-08, Vol.80 (19), p.29643-29656
• Main Authors: Hsu, Chih-Yu, Wang, Shuai, Qiao, Yu
• Format: Article
• Language: English
• Subjects: Accuracy; Art exhibits; Classifiers; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Decision trees; Deep learning; Intrusion detection systems; Machine learning; Multimedia; Multimedia Information Systems; Special Purpose and Application-Based Systems; Subsystems; Support vector machines
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11100-x

The multimedia service company, Netflix, increased the number of new subscribers during the Coronavirus pandemic age. Intrusion detection systems for multimedia platforms can prevent the platform from network attacks. An intelligent intrusion detection system is proposed for the security IP Multimedia Subsystem (IMS) based on machine learning technology. For increasing the accuracy of the classifiers, it is vital to select the critical features to construct the intrusion detection system. Two-class classifiers, including the Decision Tree, Support Vector Machine, and Naive Bayesian, are selected to evaluate intrusion detection accuracy. According to the three classifiers’ accuracy values, the most critical features are selected based on the features’ ranking orders. Six critical features are selected:Service, dst_host_same_srv_rate, Flag, Protocol Type, Dst_host_rerror_rate, and Count. Numerical comparison with state_of_the_art shows that critical features improve intrusion detection accuracy, which can be better than the deep learning method.