“SR-MLC: Scalable Resilience Machine Learning Classifiers Approach in Cyber Security

Cyber resilience is a rapidly emerging viewpoint that is attaining recognition. Unfavourable Cyber-attacks are those that oppositely influence the availability, integrity or confidentiality of IT network systems and related services and information. Prior research works have carried on data manipulation by an opponent as a concern, but their works failed to generalize the test cases. Many concentrated on devising attack vectors opposite to specific machine learning algorithms and applications, such as the Support Vector Machine (SVM) classifier. In our proposed work, an independent approach on resilience evaluation and the construction of adversary resilient classifiers using Cluster Tree Map (CTM) Algorithm is done. All data types in the domain of Cyber Network data analytics are focussed. The objective is to make an awareness of any such method capable of correctly modelling the creativeness and skill of cyber attackers and thereby developing unsupervised learning model. Better expected accuracy is attained by using Scalable Resilience Machine Learning Classifiers (SR-MLC).