Implementation of Robust Privacy-Preserving Machine Learning with Intrusion Detection and Cybersecurity Protection Mechanism

Abstract

In this work, data mining, and cloud computing problems such as privacy protection, Intrusion detection, identification system have been realizing. Social-Cybersecurity is an evolving science-focused field for characterizing, interpreting, and predicting improvements in human behavior, social, cultural, political outcomes, and development. The cyber-infrastructure is required to sustain its critical existence cyber-mediated knowledge climate under shifting circumstances and cyber threats that are immediate or imminent. When the data moves from the local cloud to another cloud, some security issues automatically arise. At this stage, an advanced robust security system with advanced encryption or decryption algorithm is necessary. At the same time, intrusion may attack the cloud for hacking or modifying the existing information. There an advanced deep learning algorithm is essential to make the cloud efficient. The Logistic net regression Optimization is proposed for cybersecurity and protection. A final performance measures, estimating accuracy,0.952 sensitivity of 0.39, Recall, F1 score 0.54 and processing time.