Intrusion Detection by Stacked Deep Ensemble Model with Entropy and Correlation Feature Set - Godala, Sravanthi; Sunil Kumar, M. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Nowadays, in order to improve the routine activities, interoperability and interconnectivity of computing systems are extensively used. In addition, it creates a way to vulnerabilities that are far beyond the reach of human control. Due to the vulnerabilities, data transfer must include cyber-security measures. Secure connectivity demands improvements to security mechanisms to counter emerging security risks and security systems to mitigate the threats. This paper proposes Intrusion Detection by Stacked Deep Ensemble Model (IDSDEM) which has three working stages. Initially, in the pre-processing stage, data normalization process is conducted to reduce the data redundancy and increases the consistency of data for further process. Afterwards feature extraction stage takes place where the features such as entropy based as well as improved correlation based features were extracted. Finally, intrusion detection is conducted where a stacked deep ensemble model which includes the classifiers like Deep Belief Network (DBN), Deep Maxout Network and Customized Convolutional Neural Network (CCNN) is employed to provide effective intrusion detection. The outcomes demonstrated that the developed IDSDEM can offer superior performance with respect to detection accuracy, precision and other measures.