Deep learning with blockchain based cyber security threat intelligence and situational awareness system for intrusion alert prediction

Abstract

Network security situation assessment (NSSA) is imperative and active defense technology in the network security situation. By examining NSSA data, one can examine the threat of network security and examine the network attack phase and hence fully grasp the complete network security situation. With the quick design of 5 G, the cloud model and Internet of things (IoT), the network platform is increasingly complicated and resulting in diversity of network threats which discover the accuracy. Thus, a new blockchain based cyber-security threat intelligence (CTI) and situational awareness system is devised for intrusion alert prediction. A blockchain-based CTI model is considered where data acquired are allowed to linear normalization. Here, the cyber situational awareness engine is used for alert segregation, which is implemented with entropy weighting power k means algorithm wherein weights generated during alert segregation are updated using Adaptive Transit Search (ATS). Then, the feature selection is implemented using hybrid Soergel and Lorentzian. The selected features are fed to Deep Maxout Network (DMN) for performing intrusion alert prediction. Finally, the cyber attack mitigation is carried out by blacklisting based on predicted result. The modified DMN outperformed with highest F-measure of 95.2%, precision of 96.9% and recall of 94.7%.