Understanding of Cyber Security Attacks with the Hybrid Architecture of Deep Neural Nets with Markov Chain Montecarlo

In this approach, we used multiple random information from the set theory to understand the proportional of information leaks if any threats happen. In this environment, we try to propose a hybrid architecture, which is a combination of convolutional neural network and decision based neural network. The main limitation, we are trying to solve to establish the nature of risk minimization in risk by the deep learning weights in its architecture. This architecture we are trying to build is hybrid in nature as the combination of different information given in cybersecurity. Due to the lack of proper dataset for cyber-security, anomaly-based approaches in intrusion detection systems are suffering from accurate deployment, analysis, and evaluation. There are several datasets such as DARPA98, KDD99, ISC2012, CIC and ADFA13 that have been used by the researchers to evaluate the performance of their proposed intrusion detection and intrusion prevention approaches