Efficient Development of Intrusion Detection Using Multilayer Perceptron Using Deep Learning Approaches

The term cyber-attack or intrusion is expanded as an unauthorized process that includes one or more of the above three components of the network system. The intrusion detection (ID) process helps the administrator of the system to build up security mechanisms that recognize the legitimate or illegitimate of the system. The illegitimate user of the network system is named an intruder which can be a person within the organization or outside the organization. IDS are constructed with the concept of observing the unauthorized behavior of the user concerning the authorized behavior activities. The deviation noted based on the comparison is considered an intrusion. Many novel techniques are developed through research to observe and identify the current activities. In this research three algorithms namely R-SVM, Adaptive Boosting and Multi-Layer perceptron have been used. From the results obtained Multi-Layer perceptron produces Accuracy of 92.3%, Precision of 0.89, Recall of 0.87 and F Measure of 0.8. The tool used is Jupyter Notebook and language used is python.